# HTP This section provides information specific to QNN HTP backend. - [API Specializations](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#api-specializations) - [Usage Expectations](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#usage-expectations) - [QNN HTP Supported Operations](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-supported-operations) - [QNN HTP Backend API](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-backend-api) - [QNN HTP Performance Infrastructure API](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-performance-infrastructure-api) - [QNN HTP Precision](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-precision) - [QNN HTP FP16 output difference between SM8550 and SM8650](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-fp16-output-difference-between-sm8550-and-sm8650) - [QNN HTP Deep Learning Bandwidth Compression (DLBC)](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-deep-learning-bandwidth-compression-dlbc) - [QNN HTP - Setting Number of HVX Threads](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-setting-number-of-hvx-threads) - [QNN HTP - Enabling the system level cache allocator](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-enabling-the-system-level-cache-allocator) - [QNN HTP Backend Extensions](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-backend-extensions) - [QNN HTP Profiling](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-profiling) - [QNN HTP Optrace Profiling](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-optrace-profiling) - [QNN HTP Analysis Summary (QHAS)](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-analysis-summary-qhas) - [QNN Context Binary size](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-context-binary-size) - [Op Writing Guidelines](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#op-writing-guidelines) - [Recommendations for Network Design](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#recommendations-for-network-design) - [Yielding and Pre-Emption](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#yielding-and-pre-emption) - [Byte-Granularity VTCM](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#byte-granularity-vtcm) - [VTCM Sharing](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#vtcm-sharing) - [SubSystem Restart (SSR)](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#subsystem-restart-ssr) - [Qmem Graph (shared_buffer only graph)](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qmem-graph-shared-buffer-only-graph) - [Graph Switching (Beta)](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#graph-switching-beta) - [Multi-Graph Switching (Beta)](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#multi-graph-switching-beta) - [Benefits of batch inference and multi-threaded inference](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#benefits-of-batch-inference-and-multi-threaded-inference) - [Hexagon NPU Runtime Driver (Windows Only)](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#hexagon-npu-runtime-driver-windows-only) - [Init Cancellation](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#init-cancellation) - [Graph Priority](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#graph-priority) - [Setting Graph Priority](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#setting-graph-priority) - [LLM native KVcache](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#llm-native-kvcache) - [MaskedSoftmax](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#maskedsoftmax) ## API Specializations This section contains information related to API specialization for the HTP backend. All QNN HTP backend specialization is available under `/include/QNN/HTP/` directory. The current version of the QNN HTP backend API is: - QNN\_HTP\_API\_VERSION\_MAJOR 5 - - QNN\_HTP\_API\_VERSION\_MINOR 39 - - QNN\_HTP\_API\_VERSION\_PATCH 0 - ## Usage Expectations 1. The sequence of calls to [QnnGraph\_addNode()](https://docs.qualcomm.com/doc/80-63442-50/topic/function_QnnGraph_8h_1a1c6363e459cb7092356572f5af1c952b.html#exhale-function-qnngraph-8h-1a1c6363e459cb7092356572f5af1c952b) to build the QNN model should be done in the node dependency order. 2. The [QnnBackend\_registerOpPackage()](https://docs.qualcomm.com/doc/80-63442-50/topic/function_QnnBackend_8h_1a95dd59ad0b59872f3649f7c363c23441.html#exhale-function-qnnbackend-8h-1a95dd59ad0b59872f3649f7c363c23441) takes in an optional parameter called ‘target’. > > > Given below are the acceptable values for ‘target’ > > > > > > > > > - “CPU” - for both linux x86 and ARM op packages > > - “HTP” - for op package that gets loaded on the HTP. > > - nullptr > > > > > > > > > > > > > > > 1. For loading context binary on ARM - Loads registered HTP op package > > > 2. For linux x86 - Registers the linux x86 op package 3. Loading a context binary generated for a different HTP arch may give indeterminate result. ## QNN HTP Supported Operations QNN HTP supports running quantized 8-bit and quantized 16-bit networks on all Qualcomm SoCs. List of operations supported by QNN HTP Quant runtime can be seen under Backend Support HTP column in [Supported Operations](https://docs.qualcomm.com/doc/80-63442-50/topic/SupportedOps.html#supported-operations) QNN HTP supports running float32 networks using float16 math on select Qualcomm SoCs. If the QNN SDK supports QNN HTP Float, the list of operations supported by HTP Float runtime can be seen under Backend Support HTP\_FP16 column in [Supported Operations](https://docs.qualcomm.com/doc/80-63442-50/topic/SupportedOps.html#supported-operations) For the QNN SDK that does support QNN HTP Float, please note that even though HTP and HTP\_FP16 are listed under separate columns in [Supported Operations](https://docs.qualcomm.com/doc/80-63442-50/topic/SupportedOps.html#supported-operations) they are a single “logical” backend. On select SoCs, QNN HTP backend library supports both quantized and float networks. The separate columns is done to distinguish the different supported operations list for quantized and float QNN HTP runtimes. ## QNN HTP Variable Batch QNN HTP supports variable batch dimension in a limited manner. The batch dimension at graph execute can be an integer mulitple of the respective dimension provided at graph prepare. All inputs and outputs tensors must have the same batch multiple. For example, if the tensor dimensions provided at graph prepare are [b,h,w,d] then graph can be executed with tensor having dimensions as [n\*b,h,w,d], where n is a positive integer. ## QNN HTP Backend API [File QnnHtpDevice.h](https://docs.qualcomm.com/doc/80-63442-50/topic/api-rst_file_include_QNN_HTP_QnnHtpDevice_h.html##file-include-qnn-htp-qnnhtpdevice-h) is the backend specialization header that goes along with [File QnnDevice.h](https://docs.qualcomm.com/doc/80-63442-50/topic/api-rst_file_include_QNN_QnnDevice_h.html##file-include-qnn-qnndevice-h). This header file allows clients to configure the QnnDevice to cater to specific use-cases. [Struct QnnHtpGraph\_CustomConfig\_t](https://docs.qualcomm.com/doc/80-63442-50/topic/structQnnHtpGraph__CustomConfig__t.html#exhale-struct-structqnnhtpgraph-customconfig-t) is defined in [File QnnHtpGraph.h](https://docs.qualcomm.com/doc/80-63442-50/topic/api-rst_file_include_QNN_HTP_QnnHtpGraph_h.html##file-include-qnn-htp-qnnhtpgraph-h) and is the backend specialization header that goes with [File QnnGraph.h](https://docs.qualcomm.com/doc/80-63442-50/topic/api-rst_file_include_QNN_QnnGraph_h.html##file-include-qnn-qnngraph-h) **QNN HTP Device Config Options (QnnHtpDevice\_CustomConfig\_t)** | Option Name | Option Description | Default | When to use | | --- | --- | --- | --- | | QNN\_HTP\_DEVICE\_CONFIG\_OPTION\_SOC | Integer value used to identify SoC model | QNN\_SOC\_MODEL\_SM8350 | Client can provide socModel to indicate which SoC is targeted | | QNN\_HTP\_DEVICE\_CONFIG\_OPTION\_ARCH | Data structure to configure a device to set the HTP Arch. The driver will use ops
that are compatible to this HTP Arch | QNN\_HTP\_DEVICE\_ARCH\_NONE | Client can provide as part of the custom config when there are multiple devices in use | | QNN\_HTP\_DEVICE\_CONFIG\_OPTION\_SIGNEDPD | Enables signed process domain. In order to use this flag, client also needs to push a signed
dsp image to target | False (Unsigned Process Domain) | Client use signed process domain. Check Hexagon SDK document for more detail. | Client can set SocModel as shown below: Refer [Qnn\_SocModel\_t](https://docs.qualcomm.com/doc/80-63442-50/topic/enum_QnnTypes_8h_1aea3262897ad2f237f9e5189419ad2214.html#exhale-enum-qnntypes-8h-1aea3262897ad2f237f9e5189419ad2214) for setting Soc Model. Note that Qnn\_SocModel\_t will be deprecated, For setting Soc Model refer to the [Supported Snapdragon Devices](https://docs.qualcomm.com/doc/80-63442-50/topic/overview.html#supported-snapdragon-devices) 1QnnHtpDevice_CustomConfig_t customConfig; 2customConfig.option = QNN_HTP_DEVICE_CONFIG_OPTION_SOC; 3customConfig.socModel = QNN_SOC_MODEL_SM8550; 4QnnDevice_Config_t devConfig; 5devConfig.option = QNN_DEVICE_CONFIG_OPTION_CUSTOM; 6devConfig.customConfig = &customConfig; 7const QnnDevice_Config_t* pDeviceConfig[] = {&devConfig, NULL}; Copy to clipboard Client can set Htp arch as shown below: Refer [QnnHtpDevice\_Arch\_t](https://docs.qualcomm.com/doc/80-63442-50/topic/enum_QnnHtpDevice_8h_1a0ed976142af98a86143459dfd326f717.html#exhale-enum-qnnhtpdevice-8h-1a0ed976142af98a86143459dfd326f717) for setting Htp Arch. 1QnnHtpDevice_CustomConfig_t customConfig; 2customConfig.option = QNN_HTP_DEVICE_CONFIG_OPTION_ARCH; 3customConfig.arch.arch = QNN_HTP_DEVICE_ARCH_V73; 4customConfig.arch.deviceId = 0; // Id of device to be used. If single device is used by default 0. 5QnnDevice_Config_t devConfig; 6devConfig.option = QNN_DEVICE_CONFIG_OPTION_CUSTOM; 7devConfig.customConfig = &customConfig; 8const QnnDevice_Config_t* pDeviceConfig[] = {&devConfig, NULL}; Copy to clipboard Client can set signed PD as shown below: 1QnnHtpDevice_CustomConfig_t customConfig; 2customConfig.option = QNN_HTP_DEVICE_CONFIG_OPTION_SIGNEDPD; 3customConfig.useSignedProcessDomain.useSignedProcessDomain = true; 4customConfig.useSignedProcessDomain.deviceId = 0; // Id of device to be used. If single device is used by default 0. 5QnnDevice_Config_t devConfig; 6devConfig.option = QNN_DEVICE_CONFIG_OPTION_CUSTOM; 7devConfig.customConfig = &customConfig; 8const QnnDevice_Config_t* pDeviceConfig[] = {&devConfig, NULL}; Copy to clipboard **QNN HTP Context Config Options (QnnHtpContext\_CustomConfig\_t)** | Option Name | Option Description | Default | When to use | | --- | --- | --- | --- | | QNN\_HTP\_CONTEXT\_CONFIG\_OPTION\_WEIGHT\_SHARING\_ENABLED | This feature allows common weights across graphs (max 64) to be shared and stored in a single
context binary. | Weight sharing feature is disabled by default. When preparing context binary with multiple
graphs that have common weights, these weights will not be shared. | When preparing a context binary with multiple graphs that share common weights, this feature
can be utilized to reduce overall memory usage by sharing common weights. It is important to
note that shared weights include a portion of the weights enabled across the entire suite of
graphs within a context. As an example, if only a single graph is utilized and the portion of
shared weights is large, this could adversely impact RAM and ROM. The ideal usage of shared
weights is when graphs are very similar in terms of weights, allowing users to decrease RAM
and ROM usage by sharing the weights. Optimization in this area could be considered for
future releases. | | QNN\_HTP\_CONTEXT\_CONFIG\_OPTION\_REGISTER\_MULTI\_CONTEXTS | This enum allows users to associate multiple contexts to a group. When registering the first
context to a group, two values must be configured – handle to the first context (it would
always be 0 for the first one) and the maximum spill-fill buffer size. Subsequent registration
of other contexts would need to pass down the context handle of the first context registered
to a group. | Spill fill buffer is not shared across multiple contexts. Each graph creates its own buffer. | When multiple models are executed in sequence and as a result it is possible to reserve
a single spill-fill allocation that could be re-used across all the splits. This has the
benefit of reducing RAM usage for the application at negligible performance impact. This
should only be used with the `QnnContext_createFromBinary` API. | | QNN\_HTP\_CONTEXT\_CONFIG\_OPTION\_FILE\_READ\_MEMORY\_BUDGET | This feature allows users to configure the read memory budget of the deserialized binary
in megabytes (Mb). It gives a hint to the backend to load the binary in chunks, instead of
loading the entire binary to memory at once. | File read memory budget is turned off by default. In this case, the entire file is placed in
memory at once. | With this feature enabled, initialization time might be impacted. Hence, it should only be
used if peak memory usage is of concern. Users need to find the proper balance of performance
vs. memory for their specific use-case. Additionally, this config should only be used with
mmapped model buffers. Using non-mmapped model buffer will result in an undefined behavior. | | QNN\_HTP\_CONTEXT\_CONFIG\_OPTION\_IO\_MEM\_ESTIMATION | This field enables I/O memory estimation during QnnContext\_createFromBinary API when multiple
PDs are available. When enabled, it estimates the total size of the I/O tensors required by
the context to ensure sufficient space on the PD before deserialization. | I/O memory estimation is turned off by default. | This feature can help with memory registration failures in large models when multiple PDs are
available. Enabling this feature increases peak RAM usage during context initialization phase
in QnnContext\_createFromBinary, but sustained RAM remains unaffected. | | QNN\_HTP\_CONTEXT\_CONFIG\_OPTION\_DSP\_MEMORY\_PROFILING\_ENABLED | This feature allows context-related total DSP heap usage profiling as detailed in the
[DSP heap profiling](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#dsp-heap-profiling) section. | DSP heap profiling is turned off by default. In this case, no DSP profiling will occur. | With this feature enabled, profiling data will be captured upon `QnnContext_createFromBinary`
and `QnnContext_free`. | | QNN\_HTP\_CONTEXT\_CONFIG\_OPTION\_SHARE\_RESOURCES | This field enables resource sharing across different contexts during the
`QnnContext_createFromBinaryListAsync` API. When enabled, memory optimizations are applied based
on the `QNN_HTP_CONTEXT_CONFIG_OPTION_SHARE_RESOURCES_OPTIMIZATION_TYPE`. | Resource sharing is turned off by default. | This feature optimizes runtime HTP virtual address space and/or memory usage.
**Note: This feature cannot be used together with graph switching.** | | QNN\_HTP\_CONTEXT\_CONFIG\_OPTION\_SHARE\_RESOURCES\_OPTIMIZATION\_TYPE | This field is supported only when `QNN_HTP_CONTEXT_CONFIG_OPTION_SHARE_RESOURCES` is true;
otherwise, it is ignored. Refer [here](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#share-resource-configure-demo) for the available
configuration options. | When `QNN_HTP_CONTEXT_CONFIG_OPTION_SHARE_RESOURCES` is true and no input is provided for this field
`SEQUENTIAL_WITH_VA_OPTIMIZATION` is the default value. | This provides a way to customize resource sharing. More details can be found
[here](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#share-resource-configure-demo). | | QNN\_HTP\_CONTEXT\_CONFIG\_OPTION\_INIT\_ACCELERATION | This field enables initialization acceleration during `QnnContext_createFromBinary`. When enabled,
it allows hardware to utilize maximum resources for accelerating model initialization. | Init acceleration is turned OFF by default. | Whenever we initialize a graph through `QnnContext_createFromBinary`, then this feature can be
utilized to improve the initialization time. This feature may not be effective for small graphs with
a few number of ops. | | QNN\_HTP\_CONTEXT\_CONFIG\_OPTION\_SKIP\_VALIDATION\_ON\_BINARY\_SECTION | This field marks that crc32 check in Lora super adapter apply will be skipped during the
`QnnContext_applyBinarySection` API. | Skip crc32 validation on binary section is turned off by default. | When this feature is enabled, Lora super adapter apply will no longer make crc32 check for non-base
adapters (base adapters never do crc32 check). Therefore, in super adapter use cases, non-base adapter
apply time cost is improved. | | QNN\_HTP\_CONTEXT\_CONFIG\_OPTION\_REGISTER\_CONCURRENT\_RESOURCE\_SHARING | This field allows users to associate one or more contexts with the same priority to a group to enable
concurrent spill-fill and VTCM backup buffer sharing. When registering the first context to a group,
two values must be configured – handle to the first context (it would always be 0 for the first one)
and the maximum spill-fill buffer size. Subsequent registration of other contexts would need to pass
down the context handle of the first context registered to a group. If a group contains only one
context, it means all graphs within that context are sharing the same spill-fill and VTCM backup buffers.
The maximum spill-fill buffer size is directly set using the configured value provided. However,
the maximum VTCM backup buffer size is determined internally based on the device’s capabilities. | Spill-fill and VTCM backup buffers are not shared across multiple contexts. Each graph creates its
own buffer. | When multiple models are executed concurrently, it is possible to reserve a single spill-fill and
VTCM backup buffer allocation per priority, which could be re-used across all the splits.
This has the benefit of reducing RAM usage for the application at negligible performance impact.
This should only be used with the `QnnContext_createFromBinary` API. | | QNN\_HTP\_CONTEXT\_CONFIG\_OPTION\_LORA\_WEIGHT\_SHARING\_ENABLED | This feature extract out updatable weights across all graphs and put the max sized one to be shared
and stored in a single context binary as a separate weight blob. | Lora weight sharing feature is disabled by default. When preparing context binary with multiple
lora graphs, there will be no lora memory shared. | When preparing a context binary that includes multiple lora graphs, this feature can be leveraged
to reduce overall memory usage by sharing lora memories. It is important to note that the
shared lora memory contains only the updatable weights relevant to a single lora graph.
To update the contents of the lora memory, users must call the `QnnContext_applyBinarySection`
API after deserialization and before execution. Additionally, for graph switching scenarios, the HTP
backend will always re-apply the cached binary section blob prior to execution. | | QNN\_HTP\_CONTEXT\_CONFIG\_OPTION\_PREPARE\_ONLY | Enables optimizations regarding memory and performance time when graph preparation is the
only task the client wants to perform. | The prepare-only flag is turned off by default. | When clients want to simply prepare a binary, the backend does not need to set up components
required for graph execution. With this flag enabled, graph execution cannot be called. | Clients can enable weight sharing as follows: 1QnnHtpContext_CustomConfig_t customConfig; 2customConfig.option = QNN_HTP_CONTEXT_CONFIG_OPTION_WEIGHT_SHARING_ENABLED; 3customConfig.weightSharingEnabled = true; // set to false to disable weight sharing 4QnnContext_Config_t contextConfig; 5contextConfig.option = QNN_CONTEXT_CONFIG_OPTION_CUSTOM; 6contextConfig.customConfig = &customConfig; 7const QnnContext_Config_t* pContextConfig[] = {&contextConfig, NULL}; Copy to clipboard Note The Weight Sharing feature has certain requirements and limitations: 1. Only supports offline prepare on x86\_Linux platform. Online prepare and other platforms (ARM/x86\_Windows) offline prepare are not supported. 2. Only supports Hexagon v73 and onward architectures. 3. Only supports within a single PD. Sharing cross PD or cross different VTCM size and SoC is not supported. 4. Any previously generated binaries will not automatically benefit from Weight Sharing. Users are required to regenerate new serialized binary to benefit from Weight Sharing. Old serialized binaries will still work without the weight sharing feature. Clients can set shared spill-fill buffer details for multiple contexts as follows: Note This feature is only enabled for an offline prepare usecase. Information regarding spill fill size is written as part of [Struct QnnSystemContext\_BinaryInfo\_t](https://docs.qualcomm.com/doc/80-63442-50/topic/structQnnSystemContext__BinaryInfo__t.html#exhale-struct-structqnnsystemcontext-binaryinfo-t) defined in [QnnSystemContext.h](https://docs.qualcomm.com/doc/80-63442-50/topic/api-rst_file_include_QNN_System_QnnSystemContext_h.html##file-include-qnn-system-qnnsystemcontext-h). The hwInfoBlob field within the struct contains information regarding the index to the graph and respective spill fill buffer size utilized by that graph as defined in [QnnHtpSystemContext.h](https://docs.qualcomm.com/doc/80-63442-50/topic/api-rst_file_include_QNN_HTP_QnnHtpSystemContext_h.html##file-include-qnn-htp-qnnhtpsystemcontext-h). Users should figure out the maximum spill fill buffer size needed across all the contexts before proceeding to deserialize. There are two ways to achieve this: 1. Use qnn-context-binary-utility to output binary details in a JSON file. It essentially prints the content of [Struct QnnSystemContext\_BinaryInfo\_t](https://docs.qualcomm.com/doc/80-63442-50/topic/structQnnSystemContext__BinaryInfo__t.html#exhale-struct-structqnnsystemcontext-binaryinfo-t), along with HTP specific content as defined in [QnnHtpSystemContext.h](https://docs.qualcomm.com/doc/80-63442-50/topic/api-rst_file_include_QNN_HTP_QnnHtpSystemContext_h.html##file-include-qnn-htp-qnnhtpsystemcontext-h). Search for the “spillFillBufferSize” key to figure out the spill fill buffer size required for each of the graphs. 2. Add checks at runtime. Users can parse the content of the binary from [Struct QnnSystemContext\_BinaryInfo\_t](https://docs.qualcomm.com/doc/80-63442-50/topic/structQnnSystemContext__BinaryInfo__t.html#exhale-struct-structqnnsystemcontext-binaryinfo-t) struct along with HTP specific information from [QnnHtpSystemContext.h](https://docs.qualcomm.com/doc/80-63442-50/topic/api-rst_file_include_QNN_HTP_QnnHtpSystemContext_h.html##file-include-qnn-htp-qnnhtpsystemcontext-h). 1// ===== FIRST CONTEXT ===== 2QnnHtpContext_CustomConfig_t customConfig; 3customConfig.option = QNN_HTP_CONTEXT_CONFIG_OPTION_REGISTER_MULTI_CONTEXTS; 4QnnHtpContext_GroupRegistration_t groupInfo; 5groupInfo.firstGroupHandle = 0x0; // New group 6groupInfo.maxSpillFillBuffer = 30081024; // Max spill-fill buffer across contexts. Must be >0 7customConfig.groupRegistration = groupInfo; 8QnnContext_Config_t* cfgs[] = {&customConfig, NULL}; 9QnnContext_createFromBinary(..., cfgs, ..., &contextHandle, ...); 10 11// ===== SECOND CONTEXT ===== 12QnnHtpContext_CustomConfig_t customConfig2; 13customConfig2.option = QNN_HTP_CONTEXT_CONFIG_OPTION_REGISTER_MULTI_CONTEXTS; 14QnnHtpContext_GroupRegistration_t groupInfo2; 15groupInfo2.firstGroupHandle = contextHandle; // associated to above contextHandle 16groupInfo2.maxSpillFillBuffer = 30081024; // same value as above OR don't set this now 17customConfig2.groupRegistration = groupInfo2; 18QnnContext_Config_t* cfgs2[] = {&customConfig2, NULL}; 19QnnContext_createFromBinary(..., cfgs2, ..., &contextHandle2, ...); 20 21// ===== THIRD CONTEXT ===== 22QnnHtpContext_CustomConfig_t customConfig3; 23customConfig3.option = QNN_HTP_CONTEXT_CONFIG_OPTION_REGISTER_MULTI_CONTEXTS; 24QnnHtpContext_GroupRegistration_t groupInfo3; 25groupInfo3.firstGroupHandle = contextHandle; // associated to above contextHandle 26groupInfo3.maxSpillFillBuffer = 30081024; // same value as above or don't set this 27customConfig3.groupRegistration = groupInfo3; 28QnnContext_Config_t* cfgs3[] = {&customConfig3, NULL}; 29QnnContext_createFromBinary(..., cfgs3, ..., &contextHandle3, ...); Copy to clipboard Clients can set shared spill-fill and VTCM backup buffers for concurrent resource sharing as follows: Note This feature is only supported on Android for the V81 Hexagon architecture. This feature is only enabled for an offline prepare usecase. Information regarding spill fill size is written as part of [Struct QnnSystemContext\_BinaryInfo\_t](https://docs.qualcomm.com/doc/80-63442-50/topic/structQnnSystemContext__BinaryInfo__t.html#exhale-struct-structqnnsystemcontext-binaryinfo-t) defined in [QnnSystemContext.h](https://docs.qualcomm.com/doc/80-63442-50/topic/api-rst_file_include_QNN_System_QnnSystemContext_h.html##file-include-qnn-system-qnnsystemcontext-h). The hwInfoBlob field within the struct contains information regarding the index to the graph and respective spill fill buffer size utilized by that graph as defined in [QnnHtpSystemContext.h](https://docs.qualcomm.com/doc/80-63442-50/topic/api-rst_file_include_QNN_HTP_QnnHtpSystemContext_h.html##file-include-qnn-htp-qnnhtpsystemcontext-h). Users should figure out the maximum spill fill buffer size needed across the contexts for each priority before proceeding to deserialize. There are two ways to achieve this: 1. Use qnn-context-binary-utility to output binary details in a JSON file. It essentially prints the content of [Struct QnnSystemContext\_BinaryInfo\_t](https://docs.qualcomm.com/doc/80-63442-50/topic/structQnnSystemContext__BinaryInfo__t.html#exhale-struct-structqnnsystemcontext-binaryinfo-t), along with HTP specific content as defined in [QnnHtpSystemContext.h](https://docs.qualcomm.com/doc/80-63442-50/topic/api-rst_file_include_QNN_HTP_QnnHtpSystemContext_h.html##file-include-qnn-htp-qnnhtpsystemcontext-h). Search for the “spillFillBufferSize” key to figure out the spill fill buffer size required for each of the graphs. 2. Add checks at runtime. Users can parse the content of the binary from [Struct QnnSystemContext\_BinaryInfo\_t](https://docs.qualcomm.com/doc/80-63442-50/topic/structQnnSystemContext__BinaryInfo__t.html#exhale-struct-structqnnsystemcontext-binaryinfo-t) struct along with HTP specific information from [QnnHtpSystemContext.h](https://docs.qualcomm.com/doc/80-63442-50/topic/api-rst_file_include_QNN_HTP_QnnHtpSystemContext_h.html##file-include-qnn-htp-qnnhtpsystemcontext-h). In addition, the context/graph priorities within each group must be the same, and the priorities cannot be modified by either `QnnContext_setConfig()` or `QnnGraph_setConfig()` on the fly. However, if this concurrent feature is not enabled, you may have graphs with different priorities within the same context. 1// ===== CONTEXT #1: NEW GROUP ===== 2QnnHtpContext_CustomConfig_t customConfig; 3customConfig.option = QNN_HTP_CONTEXT_CONFIG_OPTION_REGISTER_CONCURRENT_RESOURCE_SHARING; 4QnnHtpContext_GroupRegistration_t groupInfo; 5groupInfo.firstGroupHandle = 0x0; // New group, can be any priority 6groupInfo.maxSpillFillBuffer = 30081024; // Max spill-fill buffer across contexts. Must be > 0 7customConfig.concurrentGroupRegistration = groupInfo; 8QnnContext_Config_t* cfgs[] = {&customConfig, NULL}; 9QnnContext_createFromBinary(..., cfgs, ..., &contextHandle1, ...); 10 11// ===== CONTEXT #2: SAME GROUP AS CONTEXT #1 ===== 12QnnHtpContext_CustomConfig_t customConfig2; 13customConfig2.option = QNN_HTP_CONTEXT_CONFIG_OPTION_REGISTER_CONCURRENT_RESOURCE_SHARING; 14QnnHtpContext_GroupRegistration_t groupInfo2; 15// Must be the same priority as CONTEXT #1 16groupInfo2.firstGroupHandle = contextHandle1; // associated with the above contextHandle1 17groupInfo2.maxSpillFillBuffer = 30081024; // same value as above OR don't set this now 18customConfig2.concurrentGroupRegistration = groupInfo2; 19QnnContext_Config_t* cfgs2[] = {&customConfig2, NULL}; 20QnnContext_createFromBinary(..., cfgs2, ..., &contextHandle2, ...); 21 22// ===== CONTEXT #3: SAME GROUP AS CONTEXT #1 ===== 23QnnHtpContext_CustomConfig_t customConfig3; 24customConfig3.option = QNN_HTP_CONTEXT_CONFIG_OPTION_REGISTER_CONCURRENT_RESOURCE_SHARING; 25QnnHtpContext_GroupRegistration_t groupInfo3; 26// Must be the same priority as CONTEXT #1 27groupInfo3.firstGroupHandle = contextHandle1; // associated with the above contextHandle1 28groupInfo3.maxSpillFillBuffer = 30081024; // same value as above or don't set this 29customConfig3.concurrentGroupRegistration = groupInfo3; 30QnnContext_Config_t* cfgs3[] = {&customConfig3, NULL}; 31QnnContext_createFromBinary(..., cfgs3, ..., &contextHandle3, ...); 32 33// ===== CONTEXT #4: NEW GROUP WITH ONLY ONE CONTEXT ===== 34QnnHtpContext_CustomConfig_t customConfig4; 35customConfig4.option = QNN_HTP_CONTEXT_CONFIG_OPTION_REGISTER_CONCURRENT_RESOURCE_SHARING; 36QnnHtpContext_GroupRegistration_t groupInfo4; 37groupInfo4.firstGroupHandle = 0x0; // New group, can be any priority 38groupInfo4.maxSpillFillBuffer = 30081024; // Max spill-fill buffer across contexts. Must be > 0 39customConfig4.concurrentGroupRegistration = groupInfo4; 40QnnContext_Config_t* cfgs4[] = {&customConfig4, NULL}; 41QnnContext_createFromBinary(..., cfgs4, ..., &contextHandle4, ...); Copy to clipboard Clients can configure read memory budget of serialized binary as follows: 1QnnHtpContext_CustomConfig_t customConfig; 2customConfig.option = QNN_HTP_CONTEXT_CONFIG_OPTION_FILE_READ_MEMORY_BUDGET; 3customConfig.fileReadMemoryBudgetInMb = 25; 4QnnContext_Config_t* cfgs[] = {&customConfig, NULL}; 5QnnContext_createFromBinary(..., cfgs, ..., &contextHandle, ...); Copy to clipboard In the example above, 25 MB chucks are loaded to memory at a time. If user sets the value to greater than the file size, min(fileSize, fileReadMemoryBudgetInMb) is used. The value should be greater than 0 and less than or equal to the file size. If a value of 0 is passed, this feature is turned off. Clients can configure I/O memory estimation as follows: 1QnnHtpContext_CustomConfig_t customConfig; 2customConfig.option = QNN_HTP_CONTEXT_CONFIG_OPTION_IO_MEM_ESTIMATION; 3customConfig.ioMemoryEstimation = true; 4QnnContext_Config_t* cfgs[] = {&customConfig, NULL}; 5QnnContext_createFromBinary(..., cfgs, ..., &contextHandle, ...); Copy to clipboard Clients can configure share resources and share resource optimization type as follows: Note This custom config needs to be set and passed as a group configuration and not as individual context configuration. `QNN_HTP_CONTEXT_CONFIG_OPTION_SHARE_RESOURCES_OPTIMIZATION_TYPE` is applied only when `QNN_HTP_CONTEXT_CONFIG_OPTION_SHARE_RESOURCES` is true; otherwise, it is ignored. The following table lists available configuration options for `QNN_HTP_CONTEXT_CONFIG_OPTION_SHARE_RESOURCES_OPTIMIZATION_TYPE`. | Option Name | Option Description | | --- | --- | | SEQUENTIAL\_WITH\_VA\_OPTIMIZATION | | | SEQUENTIAL\_WITHOUT\_VA\_OPTIMIZATION | | | CONCURRENT\_OPTIMIZATION | | 1QnnHtpContext_CustomConfig_t customListConfig[2]; 2customListConfig[0].option = QNN_HTP_CONTEXT_CONFIG_OPTION_SHARE_RESOURCES; 3customListConfig[0].shareResources = true; 4QnnHtpContext_CustomConfig_t shResOptConfig; 5customListConfig[1].option = QNN_HTP_CONTEXT_CONFIG_OPTION_SHARE_RESOURCES_OPTIMIZATION_TYPE; 6customListConfig[1].shareResOptType = SEQUENTIAL_WITH_VA_OPTIMIZATION; 7QnnContext_Config_t* cfgs[] = {&customListConfig[0], &customListConfig[1], NULL}; 8QnnContext_createFromBinaryListAsync(..., &contextParams, cfgs, ...); Copy to clipboard Clients can configure init acceleration as follows: 1QnnHtpContext_CustomConfig_t customConfig; 2customConfig.option = QNN_HTP_CONTEXT_CONFIG_OPTION_INIT_ACCELERATION; 3customConfig.initAcceleration = true; 4QnnContext_Config_t* cfgs[] = {&customConfig, NULL}; 5QnnContext_createFromBinary(..., cfgs, ..., &contextHandle, ...); Copy to clipboard Clients can configure skip validation on binary section as follows: 1QnnHtpContext_CustomConfig_t customConfig; 2customConfig.option = QNN_HTP_CONTEXT_CONFIG_OPTION_SKIP_VALIDATION_ON_BINARY_SECTION; 3customConfig.skipValidationOnBinarySection = true; 4QnnContext_Config_t* cfgs[] = {&customConfig, NULL}; 5QnnContext_createFromBinary(..., cfgs, ..., &contextHandle, ...); Copy to clipboard Clients can enable lora weight sharing as follows: 1QnnHtpContext_CustomConfig_t customConfig; 2customConfig.option = QNN_HTP_CONTEXT_CONFIG_OPTION_LORA_WEIGHT_SHARING_ENABLED; 3customConfig.loraWeightSharingEnabled = true; // set to false to disable lora weight sharing 4QnnContext_Config_t contextConfig; 5contextConfig.option = QNN_CONTEXT_CONFIG_OPTION_CUSTOM; 6contextConfig.customConfig = &customConfig; 7const QnnContext_Config_t* pContextConfig[] = {&contextConfig, NULL}; Copy to clipboard Note The Lora Weight Sharing feature has certain requirements and limitations: 1. Only supports offline prepare on x86\_Linux platform. Online prepare and other platforms (ARM/x86\_Windows) offline prepare are not supported. 2. Any previously generated binaries will not automatically benefit from Lora Weight Sharing. Users are required to regenerate new serialized binary to benefit from Lora Weight Sharing. Old serialized binaries will still work without the lora weight sharing feature. **QNN HTP Graph Config Options (QnnHtpGraph\_CustomConfig\_t)** | Option Name | Option Description | Default | When to use | | --- | --- | --- | --- | | QNN\_HTP\_GRAPH\_CONFIG\_OPTION\_OPTIMIZATION | This enum provides different HTP graph optimization options that can be used to finalize
the graph for optimum performance. | QNN\_HTP\_GRAPH\_OPTIMIZATION\_TYPE\_UNKNOWN | Client can provide this option when an optimization is desired for the graph finalize process | | QNN\_HTP\_GRAPH\_CONFIG\_OPTION\_PRECISION | An enum which defines the different precision modes supported by QNN backends | QNN\_PRECISION\_FLOAT32 | Client provides when they desire to use a specific math type in the implementation of an operation | | QNN\_HTP\_GRAPH\_CONFIG\_OPTION\_VTCM\_SIZE\_IN\_MB/QNN\_HTP\_GRAPH\_CONFIG\_OPTION\_VTCM\_SIZE | Used to define the amount of VTCM memory (in MB) to reserve and utilize | 4 | - When a client wants to use a specific (<= MAX\_SOC\_VTCM) or the maximum VTCM amount
- | | QNN\_HTP\_GRAPH\_CONFIG\_OPTION\_FOLD\_RELU\_ACTIVATION\_INTO\_CONV\_OFF | For any graph where a Convolution or Convolution like operation is followed by Relu or
ReluMinMax, the relu is folded into the Convolution operation | always fold | Clients that cannot guarantee that the quantization parameters of the Relu output exactly
reflect the range of the data after the Relu should set this flag. This will come at the
cost of performance, but perserve the requested quantization encodings | | QNN\_HTP\_GRAPH\_CONFIG\_OPTION\_SHORT\_DEPTH\_CONV\_ON\_HMX\_OFF | Run all Convolution operations using HMX instructions | always use HMX instructions | Clients that have graphs where weights are not symmetric and have Convolution with short
depths should set this flag to guarantee accurate results | | QNN\_HTP\_GRAPH\_CONFIG\_OPTION\_NUM\_HVX\_THREADS | Used to define number of HVX threads to reserve and utilize for a particular graph | 4 | When a client wants to keep aside specific number of HVX threads for other parallel work
loads | | QNN\_HTP\_GRAPH\_CONFIG\_OPTION\_WEIGHTS\_PACKING | Used to enable weights packing for a particular graph | false | This feature is currently in experimental beta release, any proposed method of usage and
behavior may change in future releases. At graph prepare, enabling this feature will cause
8-bit weights that are in the 4-bit range to be stored in the context binary as packed 4-bit,
potentially reducing the context binary size. However, please note that while this may reduce
the size of a context binary, it does not guarantee any performance improvements. | | QNN\_HTP\_GRAPH\_CONFIG\_OPTION\_FINALIZE\_CONFIG | This option sets the graph finalize level settings. | No explicit setting. | It is used to configure the graph finalize level. More details will be provided in a separate supplementary note. | QNN\_HTP\_GRAPH\_OPTIMIZATION\_TYPE\_FINALIZE\_OPTIMIZATION\_FLAG = 3 configuration will take into account QNN\_HTP\_DEVICE\_CONFIG\_OPTION\_SOC configuration when possible. When SOC information is taken into account, O3 configuration is expected to provide more optimal graph in most cases, but may result in less optimal graph in some cases. Also, it may yield possible larger context binary size and hence possible degradation on graph loading time. Note Its recommended to refer Hexagon SDK documentation prior to following section as significant functionality described here, inherently uses Hexagon SDK APIs. If the user specifies both QNN\_HTP\_DEVICE\_CONFIG\_OPTION\_SOC and QNN\_HTP\_DEVICE\_CONFIG\_OPTION\_ARCH , the HTP backend driver uses the QNN\_HTP\_DEVICE\_CONFIG\_OPTION\_SOC configuration and ignores the QNN\_HTP\_DEVICE\_CONFIG\_OPTION\_ARCH configuration. We recommend using QNN\_HTP\_DEVICE\_CONFIG\_OPTION\_SOC instead of QNN\_HTP\_DEVICE\_CONFIG\_OPTION\_ARCH. [Typedef QnnHtpDevice\_DeviceInfoExtension\_t](https://docs.qualcomm.com/doc/80-63442-50/topic/typedef_QnnHtpDevice_8h_1a1b95c51336e38afa44dff4da80c21c76.html#exhale-typedef-qnnhtpdevice-8h-1a1b95c51336e38afa44dff4da80c21c76) is a backend specific subcomponent of [Struct QnnDevice\_HardwareDeviceInfo\_t](https://docs.qualcomm.com/doc/80-63442-50/topic/structQnnDevice__HardwareDeviceInfo__t.html#exhale-struct-structqnndevice-hardwaredeviceinfo-t). Information for these structs are provided by the client for offline operation, and can be populated by a call to [QnnDevice\_getPlatformInfo()](https://docs.qualcomm.com/doc/80-63442-50/topic/function_QnnDevice_8h_1af1ad41deb166ea77b455603e3f88c992.html#exhale-function-qnndevice-8h-1af1ad41deb166ea77b455603e3f88c992) **QNN HTP Device Info Extension Options (QnnHtpDevice\_DeviceInfoExtension\_t)** | Option Name | Option Description | Default | When to use | | --- | --- | --- | --- | | QNN\_HTP\_DEVICE\_CONFIG\_OPTION\_PCIE\_DEVICE\_INFO\_EXTENSION | This structure provides info about the device that connect with PCIe bus: VTCM size (in MB), socModel,
number of NSPs on the PCIe device, signed PD support, DLBC support, device architecture | NULL | For online operation, caller should get these info from QnnDevice\_getPlatformInfo. For
offline operation, caller need to create this structure and filling the correct information for QnnDevice\_create | | QNN\_HTP\_DEVICE\_CONFIG\_OPTION\_ON\_CHIP\_DEVICE\_INFO\_EXTENSION | This structure provides info about the NSP device inside SoC: VTCM size (in MB), socModel, signed PD
support, DLBC support, device architecture | NULL | For online operation, caller should get these info from QnnDevice\_getPlatformInfo. For
offline operation, caller need to create this structure and filling the correct information for QnnDevice\_create | ## QNN HTP Performance Infrastructure API Clients can invoke [QnnDevice\_getInfrastructure](https://docs.qualcomm.com/doc/80-63442-50/topic/function_QnnDevice_8h_1ae5b2937f9fa4f720815b7380016d9d4e.html#exhale-function-qnndevice-8h-1ae5b2937f9fa4f720815b7380016d9d4e) after loading the QNN HTP library and then invoke methods that are available in [QnnHtpPerfInfrastructure.h](https://docs.qualcomm.com/doc/80-63442-50/topic/api-rst_program_listing_file_include_QNN_HTP_QnnHtpPerfInfrastructure_h.html##program-listing-file-include-qnn-htp-qnnhtpperfinfrastructure-h). These APIs allow a client to control CPU and HTP accelerator’s system settings for performance purpose. A few use-cases are: 1. Set up a voting policy by controlling the core clocks and voltage corners. 2. Set up DCVS modes to achieve different performance settings as applicable to a use-case. 3. Set up a specific RPC Control Latency per session to control CPU low power modes and reduce CPU wake-up latency impact on FastRPC. Latency critical applications recommended to vote for greater than 0 and less than 200 us; moderate latency requirement can vote for more than 200 us or by default 0 us without specific setting needed. Note Setting up number of threads for accelerator is not supported. QNN Perf Infrastructure maps directly to fast RPC features on CPU side, and on HTP side maps to HAP\_Power DCVS v3. For detailed configuration of voltage corners and DCVS modes, please refer to Hexagon SDK documentation on HAP\_power\_set API. QNN provides interface through APIs to control DSP core and bus clocks based on power and performance needs. These APIs allow programmers to adjust the DSP power usage as per the application’s power requirement, thereby providing a good balance between power consumption and performance. Performance Parameters table shows settings for various user defined performance profiles. These performance parameters are listed in [QnnHtpPerfInfrastructure.h](https://docs.qualcomm.com/doc/80-63442-50/topic/api-rst_program_listing_file_include_QNN_HTP_QnnHtpPerfInfrastructure_h.html##program-listing-file-include-qnn-htp-qnnhtpperfinfrastructure-h) and can be used to control performance settings. Usage of these performance parameters are shown below. > > > **Clock Corner Settings** - It is used to set Bus and Core operating corners for performance setting. > > > QnnHtpPerfInfrastructure_DcvsV3_t dcvsV3Config; > Copy to clipboard > > > **Bus Parameters** - Bus params is used to set the bus clock parameters. > > > dcvsV3Config.setBusParams = 1; //True to consider Bus parameter otherwise False. > dcvsV3Config.busVoltageCornerMin = DCVS_VOLTAGE_VCORNER_TURBO; > dcvsV3Config.busVoltageCornerTarget = DCVS_VOLTAGE_VCORNER_TURBO; > dcvsV3Config.busVoltageCornerMax = DCVS_VOLTAGE_VCORNER_TURBO; > Copy to clipboard > > > **Core Parameters** - Core params is used to set the core clock parameters. > > > dcvsV3Config.setCoreParams = 1; //True to consider Core parameter otherwise False. > dcvsV3Config.coreVoltageCornerMin = DCVS_VOLTAGE_VCORNER_TURBO; > dcvsV3Config.coreVoltageCornerTarget = DCVS_VOLTAGE_VCORNER_TURBO; > dcvsV3Config.coreVoltageCornerMax = DCVS_VOLTAGE_VCORNER_TURBO; > Copy to clipboard > > > **DCVS Enable** - setDcvsEnable and dcvsEnable parameters enables user to vote for DCVS participation. > > > dcvsV3Config.setDcvsEnable = 1; > dcvsV3Config.dcvsEnable = 0; // zero value means to disable dcvs > Copy to clipboard > > > **Sleep Latency** - setSleepLatency and sleepLatency parameters can be used to request for a sleep latency in micro seconds. > > > dcvsV3Config.setSleepLatency = 1; > dcvsV3Config.sleepLatency = 100; // give sleep latency value, ranges 10-65535 us > Copy to clipboard > > > **Sleep Disable** - setSleepDisable and sleepDisable parameters enables user to disable sleep (all LPM modes) in HTP. > > > dcvsV3Config.setSleepDisable = 1; > dcvsV3Config.sleepDisable = 1; // non zero value means disable sleep > Copy to clipboard > > > **Power Mode** - powerMode parameter enables user to request for a particular DCVS mode when set\_dcvs\_enable and dcvs\_enable both are set to TRUE. > > > dcvsV3Config.powerMode = QNN_HTP_PERF_INFRASTRUCTURE_POWERMODE_PERFORMANCE_MODE; > Copy to clipboard *QNN HTP Performance Infrastructure APIs* provides interface to the client to control the performance and system settings of the QNN HTP Accelerator. > > > **Create Power Config ID** - This API is used to associate unique client context so that subsequent APIs can refer to the same context using created ID. > > > Qnn_ErrorHandle_t createPowerConfigId(uint32_t deviceId, uint32_t coreId, uint32_t* powerConfigId); > > //Usage > uint32_t powerConfigId; // Below Api creates the power config id. > uint32_t deviceId = 0; > uint32_t coreId = 0; > sample_app::StatusCode sample_app::QnnSampleApp::createPowerConfigId() { > QnnDevice_Infrastructure_t deviceInfra = nullptr; > QnnInterface_t qnnInterface; > Qnn_ErrorHandle_t devErr = qnnInterface.QNN_INTERFACE_VER_NAME.deviceGetInfrastructure(&deviceInfra); > if (devErr != QNN_SUCCESS) { > QNN_ERROR("device error"); > return StatusCode::FAILURE; > } > QnnHtpDevice_Infrastructure_t *htpInfra = static_cast(deviceInfra); > QnnHtpDevice_PerfInfrastructure_t perfInfra = htpInfra->perfInfra; > Qnn_ErrorHandle_t perfInfraErr = perfInfra.createPowerConfigId(deviceId, coreId, &powerConfigId); > if (perfInfraErr != QNN_SUCCESS) { > QNN_ERROR("createPowerConfigId failed"); > return StatusCode::FAILURE; > } > return StatusCode::SUCCESS; > } > Copy to clipboard > > > **Set Power Config** - This API allows client to set up system power configuration that will enable different performance modes. > This API uses HAP\_power\_dcvs\_v3\_payload struct to config HAP power parameters. Detailed HAP power parameters description please > refer to Hexagon SDK HAP\_power\_dcvs\_v3\_payload documentation. setPowerConfig API below has settings which gives high performance, > users can experiment with different settings according to their requirements. > > > Qnn_ErrorHandle_t setPowerConfig(uint32_t powerConfigId, const QnnHtpPerfInfrastructure_PowerConfig_t** config); > > //Usage > sample_app::StatusCode sample_app::QnnSampleApp::setPowerConfig() { > QnnDevice_Infrastructure_t deviceInfra = nullptr; > QnnInterface_t qnnInterface; > Qnn_ErrorHandle_t devErr = qnnInterface.QNN_INTERFACE_VER_NAME.deviceGetInfrastructure(&deviceInfra); > if (devErr != QNN_SUCCESS) { > QNN_ERROR("device error"); > return StatusCode::FAILURE; > } > QnnHtpDevice_Infrastructure_t *htpInfra = static_cast(deviceInfra); > QnnHtpDevice_PerfInfrastructure_t perfInfra = htpInfra->perfInfra; > > QnnHtpPerfInfrastructure_PowerConfig_t powerConfig; > memset(&powerConfig, 0, sizeof(powerConfig)); > powerConfig.option = QNN_HTP_PERF_INFRASTRUCTURE_POWER_CONFIGOPTION_DCVS_V3; > powerConfig.dcvsV3Config.dcvsEnable = 0; //1- To enable Dcvs and consider dcvs power mode, 0- To disable dcvs > powerConfig.dcvsV3Config.setDcvsEnable = 1; > powerConfig.dcvsV3Config.contextId = powerConfigId; //use the power config id created > > // refer QnnHtpPerfInfrastructure.h > powerConfig.dcvsV3Config.powerMode = QNN_HTP_PERF_INFRASTRUCTURE_POWERMODE_PERFORMANCE_MODE; > powerConfig.dcvsV3Config.setSleepLatency = 1; //True to consider Latency parameter otherwise False > powerConfig.dcvsV3Config.setBusParams = 1; //True to consider Bus parameter otherwise False > powerConfig.dcvsV3Config.setCoreParams = 1; //True to consider Core parameter otherwise False > powerConfig.dcvsV3Config.sleepDisable = 1; //True to disable sleep, False to re-enable sleep > powerConfig.dcvsV3Config.setSleepDisable = 1; //True to consider sleep disable/enable parameter otherwise False > > //Set Sleep latency parameter > powerConfig.dcvsV3Config.sleepLatency = 40; // set dsp sleep latency ranges 10-65535 micro sec, refer hexagon sdk > > //set Bus Clock Parameters (refer QnnHtpPerfInfrastructure.h) > powerConfig.dcvsV3Config.busVoltageCornerMin = DCVS_VOLTAGE_VCORNER_MAX_VOLTAGE_CORNER; > powerConfig.dcvsV3Config.busVoltageCornerTarget = DCVS_VOLTAGE_VCORNER_MAX_VOLTAGE_CORNER; > powerConfig.dcvsV3Config.busVoltageCornerMax = DCVS_VOLTAGE_VCORNER_MAX_VOLTAGE_CORNER; > > //set Core Clock Parameters (refer QnnHtpPerfInfrastructure.h) > powerConfig.dcvsV3Config.coreVoltageCornerMin = DCVS_VOLTAGE_VCORNER_MAX_VOLTAGE_CORNER; > powerConfig.dcvsV3Config.coreVoltageCornerTarget = DCVS_VOLTAGE_VCORNER_MAX_VOLTAGE_CORNER; > powerConfig.dcvsV3Config.coreVoltageCornerMax = DCVS_VOLTAGE_VCORNER_MAX_VOLTAGE_CORNER; > > // Set power config with different performance parameters > const QnnHtpPerfInfrastructure_PowerConfig_t *powerConfigs[] = {&powerConfig, NULL}; > > Qnn_ErrorHandle_t perfInfraErr = perfInfra.setPowerConfig(powerConfigId, powerConfigs); > if (perfInfraErr != QNN_SUCCESS) { > QNN_ERROR("setPowerConfig failed"); > return StatusCode::FAILURE; > } > return StatusCode::SUCCESS; > } > Copy to clipboard > > > **Destroy Power Config ID** - This API allows client to destroy power configuration ID which was created earlier. > > > Qnn_ErrorHandle_t destroyPowerConfigId(uint32_t powerConfigId); > > //Usage > sample_app::StatusCode sample_app::QnnSampleApp::destroyPowerConfigId() { > QnnDevice_Infrastructure_t deviceInfra = nullptr; > QnnInterface_t qnnInterface; > Qnn_ErrorHandle_t devErr = qnnInterface.QNN_INTERFACE_VER_NAME.deviceGetInfrastructure(&deviceInfra); > if (devErr != QNN_SUCCESS) { > QNN_ERROR("device error"); > return StatusCode::FAILURE; > } > QnnHtpDevice_Infrastructure_t *htpInfra = static_cast(deviceInfra); > QnnHtpDevice_PerfInfrastructure_t perfInfra = htpInfra->perfInfra; > > Qnn_ErrorHandle_t perfInfraErr = perfInfra.destroyPowerConfigId(powerConfigId); > if (perfInfraErr != QNN_SUCCESS) { > QNN_ERROR("destroyPowerConfigId failed"); > return StatusCode::FAILURE; > } > return StatusCode::SUCCESS; > } > Copy to clipboard > > > Apart from the above APIs, the user can use RPC polling and control latency for better performance in high performance modes. > > > **RPC Polling and Latency Settings** - rpcPollingTimeConfig parameter can be used to request for a rpc polling time in micro seconds, > rpcControlLatencyConfig parameter can be used to reduce CPU wakeup delays. > > > sample_app::StatusCode sample_app::QnnSampleApp::setRpcLatencyAndPolling() { > QnnDevice_Infrastructure_t deviceInfra = nullptr; > QnnInterface_t qnnInterface; > Qnn_ErrorHandle_t devErr = qnnInterface.QNN_INTERFACE_VER_NAME.deviceGetInfrastructure(&deviceInfra); > if (devErr != QNN_SUCCESS) { > QNN_ERROR("device error"); > return StatusCode::FAILURE; > } > QnnHtpDevice_Infrastructure_t *htpInfra = static_cast(deviceInfra); > QnnHtpDevice_PerfInfrastructure_t perfInfra = htpInfra->perfInfra; > > // set RPC Control Latency > QnnHtpPerfInfrastructure_PowerConfig_t rpcControlLatency; // refer QnnHtpPerfInfrastructure.h > memset(&rpcControlLatency, 0, sizeof(rpcControlLatency)); > rpcControlLatency.option = QNN_HTP_PERF_INFRASTRUCTURE_POWER_CONFIGOPTION_RPC_CONTROL_LATENCY; > rpcControlLatency.rpcControlLatencyConfig = 100; // use rpc control latency recommended 100 us, refer hexagon sdk > const QnnHtpPerfInfrastructure_PowerConfig_t *powerConfigs1[] = {&rpcControlLatency, NULL}; > > Qnn_ErrorHandle_t perfInfraErr = perfInfra.setPowerConfig(powerConfigId, powerConfigs1); // set RPC latency config on power config ID created > if (perfInfraErr != QNN_SUCCESS) { > QNN_ERROR("setPowerConfig failed"); > return StatusCode::FAILURE; > } > > // set RPC Polling > QnnHtpPerfInfrastructure_PowerConfig_t rpcPollingTime; // refer QnnHtpPerfInfrastructure.h > memset(&rpcPollingTime, 0, sizeof(rpcPollingTime)); > rpcPollingTime.option = QNN_HTP_PERF_INFRASTRUCTURE_POWER_CONFIGOPTION_RPC_POLLING_TIME; > rpcPollingTime.rpcPollingTimeConfig = 9999; // use rpc polling time recommended 0-10000 us > const QnnHtpPerfInfrastructure_PowerConfig_t* powerConfigs2[] = {&rpcPollingTime, NULL}; > > Qnn_ErrorHandle_t perfInfraErr = perfInfra.setPowerConfig(powerConfigId, powerConfigs2); // set RPC polling config on power config ID created > if (perfInfraErr != QNN_SUCCESS) { > QNN_ERROR("setPowerConfig failed"); > return StatusCode::FAILURE; > } > return StatusCode::SUCCESS; > } > Copy to clipboard Note 1. RPC Latency and Polling is not supported on QNX platforms 2. For detailed information on all the above performance setting parameters refer hexagon sdk documentation. When RPC polling is enabled, the user may further enable adaptive polling for better performance, especially for large models. **Adaptive Polling Time** - adaptivePollingTimeConfig parameter allows users to set the minimum threshold for inference time to determine if adaptive polling should be activated. adaptivePollingTimeConfig parameter can be used to save CPU power by skipping unnecessary RPC polling, and saves RPC time by waking up the CPU just in time to poll for a very short period of time. > > > sample_app::StatusCode sample_app::QnnSampleApp::setAdaptivePollingTime() { > QnnDevice_Infrastructure_t deviceInfra = nullptr; > QnnInterface_t qnnInterface; > Qnn_ErrorHandle_t devErr = qnnInterface.QNN_INTERFACE_VER_NAME.deviceGetInfrastructure(&deviceInfra); > if (devErr != QNN_SUCCESS) { > QNN_ERROR("device error"); > return StatusCode::FAILURE; > } > QnnHtpDevice_Infrastructure_t *htpInfra = static_cast(deviceInfra); > QnnHtpDevice_PerfInfrastructure_t perfInfra = htpInfra->perfInfra; > > // set adaptive polling time > QnnHtpPerfInfrastructure_PowerConfig_t adaptivePollingTime; // refer to QnnHtpPerfInfrastructure.h > memset(&adaptivePollingTime, 0, sizeof(adaptivePollingTime)); > adaptivePollingTime.option = QNN_HTP_PERF_INFRASTRUCTURE_POWER_CONFIGOPTION_ADAPTIVE_POLLING_TIME; > adaptivePollingTime.adaptivePollingTimeConfig = 1000; > const QnnHtpPerfInfrastructure_PowerConfig_t *powerConfigs[] = {&adaptivePollingTime, NULL}; > > // set adaptive polling time config on power config ID created > Qnn_ErrorHandle_t perfInfraErr = perfInfra.setPowerConfig(powerConfigId, powerConfigs); > if (perfInfraErr != QNN_SUCCESS) { > QNN_ERROR("setPowerConfig failed"); > return StatusCode::FAILURE; > } > return StatusCode::SUCCESS; > } > Copy to clipboard Note 1. Adaptive Polling can only be activated if RPC Polling has already been enabled 2. It is not recommended to enable adaptive polling for small models (e.g., < 1 ms inference time) These performance APIs can be used to boost the performance. Example application of using these APIs for performance improvement in graph execution is shown below. > > > #include > #include > #include > > void example_application () { > > ----- > std::unique_ptr app; > ----- > ----- > > app->createPowerConfigId(); // Create power config ID before voting > app->setRpcLatencyAndPolling(); // Use RPC polling and latency for high performing modes > app->setPowerConfig(); // Set the different configurations for performance settings > > ----- > app->executeGraphs(); // Execute the graphs > ----- > > app->destroyPowerConfigId(); // Destroy the power config id > ----- > ----- > } > Copy to clipboard The above example app shown is purely for usage purpose. Clients can use their own settings for performance in these APIs and use them according to their requirements. **HMX Power Settings** [QnnHtpPerfInfrastructure.h](https://docs.qualcomm.com/doc/80-63442-50/topic/api-rst_program_listing_file_include_QNN_HTP_QnnHtpPerfInfrastructure_h.html##program-listing-file-include-qnn-htp-qnnhtpperfinfrastructure-h) allows setting HMX votes manually. To vote manually for both HVX and HMX, user can send different power configurations (PowerConfig’s) as shown below. The API design allows for one single call to set all power parameters. > > > Qnn_ErrorHandle_t setPowerConfig(uint32_t powerConfigId, const QnnHtpPerfInfrastructure_PowerConfig_t** config); > > //Usage > sample_app::StatusCode sample_app::QnnSampleApp::setPowerConfig() { > QnnDevice_Infrastructure_t deviceInfra = nullptr; > QnnInterface_t qnnInterface; > Qnn_ErrorHandle_t devErr = qnnInterface.QNN_INTERFACE_VER_NAME.deviceGetInfrastructure(&deviceInfra); > if (devErr != QNN_SUCCESS) { > QNN_ERROR("device error"); > return StatusCode::FAILURE; > } > QnnHtpDevice_Infrastructure_t *htpInfra = static_cast(deviceInfra); > QnnHtpDevice_PerfInfrastructure_t perfInfra = htpInfra->perfInfra; > > ------- > > // Initialize the power config and select the voltage corner values for the performance settings > QnnHtpPerfInfrastructure_PowerConfig_t powerConfig; > memset(&powerConfig, 0, sizeof(powerConfig)); > > powerConfig.option = QNN_HTP_PERF_INFRASTRUCTURE_POWER_CONFIGOPTION_DCVS_V3; > powerConfig.dcvsV3Config.dcvsEnable = 1; //1- To enable Dcvs and consider dcvs power mode, 0- To disable dcvs > powerConfig.dcvsV3Config.setDcvsEnable = 1; > powerConfig.dcvsV3Config.contextId = powerConfigId; //use the power config ID created > > // refer QnnHtpPerfInfrastructure.h > powerConfig.dcvsV3Config.powerMode = QNN_HTP_PERF_INFRASTRUCTURE_POWERMODE_PERFORMANCE_MODE; > powerConfig.dcvsV3Config.setSleepLatency = 1; //True to consider Latency parameter > powerConfig.dcvsV3Config.setBusParams = 1; //True to consider Bus parameter > powerConfig.dcvsV3Config.setCoreParams = 1; //True to consider Core parameter > powerConfig.dcvsV3Config.sleepDisable = 1; //True to disable sleep, False to re-enable sleep > powerConfig.dcvsV3Config.setSleepDisable = 1; //True to consider sleep disable/enable parameter > > //Set Sleep latency parameter > powerConfig.dcvsV3Config.sleepLatency = 40; // set dsp sleep latency ranges 10-65535 micro sec, refer hexagon sdk > > //set Bus Clock Parameters (refer QnnHtpPerfInfrastructure.h) > powerConfig.dcvsV3Config.busVoltageCornerMin = DCVS_VOLTAGE_VCORNER_MAX_VOLTAGE_CORNER; > powerConfig.dcvsV3Config.busVoltageCornerTarget = DCVS_VOLTAGE_VCORNER_MAX_VOLTAGE_CORNER; > powerConfig.dcvsV3Config.busVoltageCornerMax = DCVS_VOLTAGE_VCORNER_MAX_VOLTAGE_CORNER; > > //set Core Clock Parameters (refer QnnHtpPerfInfrastructure.h) > powerConfig.dcvsV3Config.coreVoltageCornerMin = DCVS_VOLTAGE_VCORNER_MAX_VOLTAGE_CORNER; > powerConfig.dcvsV3Config.coreVoltageCornerTarget = DCVS_VOLTAGE_VCORNER_MAX_VOLTAGE_CORNER; > powerConfig.dcvsV3Config.coreVoltageCornerMax = DCVS_VOLTAGE_VCORNER_MAX_VOLTAGE_CORNER; > > -------- > > QnnHtpPerfInfrastructure_PowerConfig_t powerConfigHMX; > memset(&powerConfigHMX, 0, sizeof(powerConfigHMX)); > > powerConfigHMX.option = QNN_HTP_PERF_INFRASTRUCTURE_POWER_CONFIGOPTION_HMX_V2; > powerConfigHMX.hmxV2Config.hmxPickDefault = 0; // 1- HMX vote will scale with Dcvs Corner, 0- HMX vote needs to specified manually > powerConfigHMX.hmxV2Config.hmxPerfMode = QNN_HTP_PERF_INFRASTRUCTURE_CLK_PERF_HIGH; //select max freq at target voltage corner, refer QnnHtpPerfInfrastructure.h > > //set HMX clock parameters (refer QnnHtpPerfInfrastructure.h) > powerConfigHMX.hmxV2Config.hmxVoltageCornerMin = DCVS_EXP_VCORNER_TUR; > powerConfigHMX.hmxV2Config.hmxVoltageCornerTarget = DCVS_EXP_VCORNER_TUR; > powerConfigHMX.hmxV2Config.hmxVoltageCornerMax = DCVS_EXP_VCORNER_TUR; > > const QnnHtpPerfInfrastructure_PowerConfig_t *powerConfigs[] = {&powerConfig, powerConfigHMX, NULL}; > Qnn_ErrorHandle_t perfInfraErr = perfInfra.setPowerConfig(powerConfigId, powerConfigs); > if (perfInfraErr != QNN_SUCCESS) { > QNN_ERROR("setPowerConfig failed"); > return StatusCode::FAILURE; > } > return StatusCode::SUCCESS; > } > Copy to clipboard Note The API for HMX power setting has few limitations as listed below: 1. Only supports Hexagon v75 and later architectures. 2. To set the HMX vote, the client must create DcvsV3 Context ID (powerConfig ID) first by calling createPowerConfigId() and use this powerConfig ID to set the HVX vote. The client can then use the same powerConfig ID to request the HMX vote either in the same call or different call. 3. No independent HMX vote will be allowed from QNN API, the client will only be able to vote for the HMX when there is an active HVX vote. 4. If no HVX vote is detected for a powerConfig ID, the HMX vote will be denied with error INVALID\_INPUT. 5. If no HMX vote is provided for a powerConfig ID, the default HMX vote will be applied (see the table below). 6. Once the client places an explicit HMX vote, it is the client’s responsibility to set hmxPickDefault and make another call to setPowerConfig() if default behavior is desired. 7. HMX vote change or revert to default can be applied, provided the context ID has a valid HVX vote. 8. When destroyPowerConfigId() is called with powerConfig ID, all votes associated with that context ID will be removed. | HVX | HMX | HMX Pick Default | Operational Validity | | --- | --- | --- | --- | | Vote Applied | Vote Applied | No | Valid | | No Vote | Vote Applied | N/A | Invalid | | Vote Applied | No vote | Yes | Valid | | Vote Removed | Vote Removed(Automatic) | N/A | Valid | **Use Case Examples** > > > `Voting at Every Inference` - This case depicts simple use case of setting certain performance setting (possibly higher > performance configuration) before executing inference request, followed by another performance setting (possibly lower > performance configuration). Figure below shows the call flow of setting performance setting at every inference. > > > ![../../_static/resources/htp_other_performance_profile.png](data:image/png;base64,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) > > > `Sustain Setting for Multiple Inference` - This case depicts the sustenance of performance setting (possibly higher performance configuration) > for multiple inferences. This can be achieved using system timer. Client can start a timer for certain duration > (higher than expected time between successive inferences) after setting performance vote (possibly higher performance). > This vote gets reset (possibly with lower performance) either when timer expires or when client requests to change > the performance settings. Figure below shows the call flow of sustaining performance setting for multiple inferences. > > > ![../../_static/resources/htp_sustained_performance_profile.png](data:image/png;base64,UklGRtAuAABXRUJQVlA4TMMuAAAv18GEAI04bttGkGDf/eT+G86MJ9tBRP8nQF8HcwmI+ERsSdgXKcBFMJIcgsJcml2wzNzigXPnFHbHk9TlorIDFKiy5Sl5boCIxNYxCcAlI43kN4iS23xB5Q7cMm85qGLgrv3/Rjo3mYIKSSRBYupcSQL8YFezAxSyF5RAilQBhcAnDDCS2FD7VSoW/CR1VPmIw7cTtW3bMPz/8vSsBNk2RTjC/OmOIBBI8qccYYT+Ew1DWJd4ApM4XIAequBAFF6mFMqY+3Z6x9VhFgQBkwb651YHf4CIUOC2jXLMV9grlGbPLBnltJvlhtiAH2+aA/RV4PKOA0kMUUi5yjdA2zwx5ZOHgFs4NFTJL/wFykQok2gyYrjIX47Yi3ld7rZyqMxrhWH8kLtRY32bhWfatleupSw0Z2aE97DDHe5w/OxQ4Q53eEI8OzzhhCfEs8MTnvCEeMbPDvHejB+8N+NHOJ3g+9Za7fSphTm6U6L+B/0TGu8RNgRlClRTqlJBNMXWoZSxq1qT0kHjPSvChmQL76Fr/kDjbVPCnVQHb7ooQQ2RE0YRTifG6AfMrmpc6CF6/0FnpI3NVMJDpggPwo03mlM7w+zBe7qUTZVWdYp9iUjgSJIkSYluFgixxBb3/68ascUSk2UOKJIkiY1qpQUNBy7U/19keFBwYMVpbcUE6JH/37FtSXlorbXWWpWJBlNraWJqKLPNNsvEbBMNJqaGa5b5zDLLbLNMtNr7nHPr3vP23RtVvxmsGawhpIVHJNIk0mvdGVEDqET4+fwWy0SvSaD+Q1gzyAkUkTPItlAt0kckypKJstPCUqsHwAT+M1hDWCizIgksTdqoRF07EX4OAJHx7H6xZvDHh+JIkiTJ5g6YsOH8/0UDGzYsGHPsyokbSZLkYQkLNly4/3/VwIK5p2ICzgbG/D/m/7S/yts5eQs/nW/JU5Efzzv5nEC+JPpD+Ybw03kXU2RAnvAOviDfCXoBEoAfIHzGXiyQI81svX4qEPw4OCL9WH4oeEr7B6GAt2H09H3ovyL3kKoJF4EA/AB5gMizdhzMFNU9UqVmAo7D7lZGY3a5lwVdALox6VsBy5LEBvGkYTw0eQNN/L1Aod4KanuI+ppYYzUbUU6MSAhirBUAdmpb9EdkhUTewmHX2hsE2Ok4/FGn16ipFubo7iHjh+CBo77j+GCOZw6z/SH4ju7aj4KBvczcsEp9CO6/xeYCAMDAVDedg9c2lSjJh6If44o7OVVWerPo742fyK921R79UYbEDLCrNtfxKp0v51p7peGpUsrYsd3XYi97eY0aWx6HXN+jdAwc4zmpEJ41NIpjxlylOPtjQi3ESMPSYiABSLFf1xppWGXWI2/hIjhloU5nWbPuVlrTJywd7hlWyo3sw1guaqMukdnWvcv2SgAuttqqdd68XZKJmfFB71ixlRL71Relta9N0BGxXPP9oIVMnRAsAWA7DsUmYw1b92Mqi9I/M8YjtbacdZVTkQVmAMnOAK0MnvbUg6ojlbBlT7gkxQUQg7wgycJMuKcwgyHj1h8tRFHFoZ73iKgAGBSYtmHlUUrobtQNQ8jbxUGBAoTxGp0m8s1cY07DUlEuglO25Uh7BWbTe/RejeOzkxBXP3RqqDLXcy8OADHOMx+H0+ZgvcriVRMuAwELWHAqJCrzAyD3OZWTcSjcAJ9W6Ypxz8cEwJZLo5VH3II+XGlyF4Sg1cAARAB5rfITeKV8znylqNXY2xNA5UWWYjMrVWR569g+MzGCkESSUgiACACSSQNbAgDL5nBaSEQJBBchA2AWBhhgLEVntWI+xwwwAwSMpZQT+X7aox0H6VsQAzorSMKKbGuB4WhW8cuOWVrHDBBAr1UuAwFWkpoTAMs4K0RgEhEQZB8845llAsCKXUXKyjkXt46rnQCRwNlMAgtYYW/pEpgjEBtZCSVjAZcC4u6OAoAuGStDCGLBJBYMwiYMAjjfBURinYMrPAQLoQ1eubmDWyn06rVJoAAgwNmaLBhyIgIBRCAQgYxzA/+sBAYAtbSSHDmGO+Gl6KnqVFDKxWzYoEkDmuZtai4BZoAZBBDAAAEA4TwDYAIIDyfcqQDAbKUAxACIwQSACUxgwmtzcPZk6XcqQALWJyGwI4TAmpRjbsadnTwj8xk0uXFAAeQ4VYO3e8ZLE3Pq+qVXumNagJ7wwkiZwr0/E3jNz3A2rMYBHRB+ACdtHjqHuVWL0x1xueRDzs/I3RzwQIV7ksPdAS+OYcal6nD+s3Dy8EB4j/9HlNXpnp8nXtjccLsjMK0BCpsdB1ubbgVZLGVbdHKQPEDwGcTnkSpu33sa2BP6iU/kuSMcvLU6GB6A+OjzaJWsl9N2HUg1PTSOX7HE9TW+wl77ErX042MUgpuf2vUQXR6pGMefQo+TE725vXkawsiwb27D5fvG8+NGrmx6/IYYhlZGBJUhcL1jx4xKjhjzfz78jTfu2GnqqBJ/yfZzxmd4YiT9sRu7lAVdO+Lb4i2df7XDLjBoDtoT4lBPqL4XGb+YWFEnFgSxxAAs2H9F7uOT5qcgVp2zos6578ZXzQJUK3HCRs+hrJ3WrHkhSdN8koR9rGMAMEGNC9+11+A5PsyOM5+pp4LPnH43TwA8A9EOikNLtlRVyNWjxq9RzN560mwHZMSyU2bBKk1Ao0ZkkjdZbTt/IJNkWdeajPiMU5BRRdidx9nKRDFJCrVedzxeGm8jDsWWczMWWcuPmH1NYvbW7u0Yo2eyKY2UtiB+simZfvJCXSGtg905xdBnh2/HedOxbkOqsinORsUDflM281WTxlsU0B60IneyAFx37IXix2sfh6M8ilWuGQypxloPHeopY/F66VtVL3w0YTxM3kzKjO2YHTbrfdbaQpzhTvZUK/iJXTvVpJd6u6ZQitBtxJmmFgBisZWeH602G47OKrBlMABbKw8ALJXLk29JFDIcfTc4TPkEMESYIJa8laAxMpsjC0QWgZUCxIosdnDmzZ6Y2me7PFqMYVoAtgzgUPA27gKBHbZucE6TnTfRBm7Mj2OpScCW/GHzMEUYoTH2GKuprky04zhtaBW6bZBTwJ0YgB1rWs2MR1t4eHIuEWbvCZgy1fjgArjl3AovNYcsDeCWRgAxIQra2ot0QnAlN94BzSzKq7kSr0pVgbVxgO92JjDGEXmExYtm0CDMAGMGkOPsjKcdgdlIpoAGMeIsz5PPANvkoLYH95iv6hQRHJpN75uMcQ3UXlsI2Vag2L1m6qsGpXGJLC965q7xM+gTaM2hqNYXyFvGAK0zwxPsXIKapz8AXFGXth2qtEC+M2rtNJsvzFQg0QiHfhCI8x548gj7gr75Ds0TU1zquxnz/0vQyfgo5jNfsGXwhSfJlIglKSMuJpoIFeO4IBnm34k8aPJvH0/Sjzvzt19VTE94t7G6jFTXr2k/ga22Z0Dtdl0svqHdwzpzbqXa1VBxTdIw1vohjOUts4yz7/+KW5cviXl3JV93GVpz3/4MYtu9odCqOlgcRys2eQ5pHiiqhNXeRicN1nUPufdbA89gXY87zfLxga79iY0r2rJRd7BWeW9yicCO8WovPzMQxkOxHZFGxnMucFtlTuctiaI4uHqSKNYbmjmU0JEysv97w+VP+XuGBkY2stnbcDJOC8PvmQMgVXB7S3eJUMXzxTZi56nchT2xeYIvoxs9rCgZmra2HiAWS1yN68GABcGyMN9GAPCGxF3Q0950WtUDmhkgJ5s1ud4ktONuAsleo40rwJ26laCWeZ1MtSknu7dx6keGHyXba5Zg+y0EVYdjXXxRjmFex/xmgjYLAnakGVAGBACspF6yKQmglCM5XOcbT9niPG6kAjAAiIgF0uAczqaVCy5TDkBEOBhjQBAR3EoJzGAwsRtmpWIdYJhBx0RtE6SOcL4UnueWO8cM5nuIvWHYdqKBMTKEN2EwPIMqRgUDYbJgxgQG9x4KI2O8y/vxyEYxEicWsICFYSHMH/eGBz7uTYBZPGAFDAsBI4EhDDlJyaR5oNGLY8NDJb3V/AqcffP1/0UKT9+q3OGW6eYHrU0c6LVvXxXL8c97HBjfSJlKURjdDPFh8e/vT3z2EWqdlI3GKhVViyrO/4FPvoe8+Jkex9AcxgjwC8bXTN665nOmcu5LppouqDrN9FiNGL9T/Xj9TnN/zsL532wvqTRwo/mFKKXmXtYqfsjhvOiTAw7yKg0D8IlsJLwS5+QJYdJ8MdlFcmn0i9A+rsSAUQSkYOsZwkgBQAqAxdCNdoUn9lwhS4xElKrZioAZnglHUvvRXExAbMsSs3rMxBHGtcHClcWsN+McYdBqE53VxoeuHpVn0LQLOiXGW+V3b3NmInMgLam2baxz4gMXMwsol3UI7Uf95kdre2F4kRQIQDaP/WrSfGSbIj/7xCWrQen9GFV2mMMFAFynZUw1VLB7h3k7IibTxVLZKvdV5hnu8KpKd0goSZvkCMkUlQHQ81HWrD5wNmeOKm5kh2RNql0Y6952eY3kTN7AbNWgxtYTI4u1US0r4GeDGDNhhlNMvhIGmJUNhUkeygojwEPbO3KhZoVmCFvBadw9CXJ1J0ty7zhsZtfTXBEHPxk9jLVGCAoiVh1HsAKBQCACb6VOyyAigmPIBaTYEQDPCg6XsT0Y8Pu0Z4XlXKXc3gmOtfEtbXps2n7DbJuY2mEUCgwKrgaTAgBmxVx7p9iQ4qDUITXybGcdyWJKChe0PY5lQG3xot0CG4ACaJABQHsSkePsBgAdzucAkE2Z1bigj0ONARB+4Xpmrf+S8lE4a/evqb+5uaB2LD2umnX51uP8BznDn7Pzj3u+nLRTuHL640Prpzevn3nzHXV4XM5Ng6snvxcMzQM4hgijzh6Dc2p6NsPSHIVy8XFa6MoRi2+Y/ZJzT0IDGFhuikOlbW0aevH7uZyxKdl7WWYAunjLLExF2Gw5lLRLz3PzgucPnMpeDv1xH4OIu3ut2mq9gFNz2PbBZZpfpMyYxE9e6glI44F69ThqwLpZ9x5mjOwDRAgmuWAn1OlGm9FayakIwLxtW2R+cTpqu+/FnjY77dOAemTGYOxmKerWmXrHEMfCTNalQQ+NAS13egvUl5VWSw21qI/6aQDNKE2P+qif2tRPDbWoRS0yZQC4m6ra8qM1JU2LpkoNBRRQUEABBRTQAJo5fGZpCt1mgha1NRXNyo/keEy+shDAmd1L1dGULNKgWzPV0Z5c6BUoRKrdmCBWJaTaMlL8TDEExBAQQADBFMIrqVT8FcCIs2IZj7cVRWxiEZtYYjHEIoZYxAipBFWyOKhlhqPgJLBr/XiQCKwiyhkMOUgcWi2bzkakAAdWWBAFEZVODRByqxgBhYBGNg8JQNps7pcPPFpR45ibHNvBCFbBHF4L2AtIHZCUNmm0D8lqczigPaYaDqrciZWWihbaOLMw/DhmM9V4vAXX1OUxd77BWNFCpY1Kl9Ftts3GmmhWtjSloixFFOPi8tbG+No248Vat1QtCk5tzdjuAIQfL0fvQB3GgeI2UKu2nkBODSIuxbtUGlfN3tolOu/kIXpNMzVfew55+ZpDuYxw5SxPHOm6RGoeT6aAxyi3th0qfk1w7dTBsANv+8LnVEiaEaPK5XeA3+PvAWR0aeyczWYAHVWSfvMIR9nK5XI8qtQ5jPuXX6UIVDhYvVRzqGn6XvALnPVC4Wl5E/mMa79mFoB/l37BhD4KADwxYEFiJVw4ZdZkL16SFJ2Mg4PtwlOpjMXcnJxN6xNB4wDYbdbArrKhSRQvGmTNuObhRUdGbvyk4pEykiNlUjZzekIThqmQbskm07BV1jpDqLXzBnZqqZpgxxCT9Rx344RkZKWMoJbkJgRV9ReNUFaMa8yZH7F60gBiEYsYYhGL2MQiNrGJTbKClfSWg1SrT7BFfajUDk0YKS3m6LC2sgfEox3Q1ZhYMr9n+6LRie9ZGtmiby1hMjsZ7hnVhLqvHatgnFgA/FhIBklGQKByUy2dPGLMQbJSDX81FFBDDQXUUB8NIAAkK6Z6Vr5CV+i4U0yhXLyupD1SWAOytlhDyWnunZZE82FZ2pwApiLOx05xPVJTa2+4qCuwnXJVektQGLvjwDOsaQsqYcQQQAABEEAsEkICsCEArjDbJI9XdAz3dcoxjdy1Wlv2Ro+Fj72dLdu0AjbfVM8QG0xWS2ZVYtYTYCbPTAJmAhgAMTEACcwCBiCIi5yooYYaalMftaF+mrZMVky1KVY1YCIAMBTkvY/WBWh7xYmxlj56VwcuSwMaM6+N6QEpoY5cLf1R7fURTWOAfOnVnnVDNWlrIQILUUdpBJZdGY4cHhYRTLhYNw/AV0YX3QdeXDAZaIH2I/N8JLaZT9koyLyNAOwOlcme+ZRhF5k9oIoDc8uNuDkJQkMzt0ELg3OXV9zGwyOliEtWAOyjWnQtuCrGDgBmABpQQETz+geLU4oyIAeQlcZHvmiA1FPbAIJrpXl7fczvxNnfYN4xjbiTc46Mi5aHMosK18yjeNtx3tJ5/cz/+DW7vwsKl25YcoXrpsgbwG8B8MrVJ32Sl3tcvhmun6/Ebwf+Pc4y3hPrcnNEUc46IZGKRcT5pnYaZbEkZcTFhERskjAVTiQtul0KFbXOrLvQtJQjtktuV+OMdV8TsZ+IFmMHi6LHWL0naVhYmZ9uh0K30jF+UkOtbQ2kbolVnoKmdCq43doRHMj6n3Xbx8t4x8G6W/s7B5Jk3ZBzpbQQLm6nIy4STvY196Zi9JMrVlPruJ3+/L6D42qRx7Cl6GJF+Z97JxWjn/nySa0TLtOVOa1gqqLc3umifhnc6U/bkjkUB1fWdR5TFeX2WWZ6HD9ujBRfZJ6Gy589zqGBMeb/Mf8/+k/Y/LVN7DMnz4nf1t1e096OTcczItM52eVIWYHvw1KXx1VDcousA1jS4f1hIfUEpMOfsaOHP7zUE2DHCWN3YDoEkyczZofoMgx9Al8tVogiNSSQBJAMAyT5Y4gmCJaKrQ6rak/MEHxWoRGxQQ9uiAw7Gh7aPUfco3YWMIA8VX0jb14n+mD+OZC1WUtgJZ0aRH30nI0LK22T5zYPqhyr1g+55OGk9KvhYk1l603v9WJaR2ukcbKMYFu12eVw4icBxEcAsRmAUBx79XHXCY7/D8UNfNUnpeoXUi2+ogAwgzSpQGytkObgAEBK7mCLbSxaoNmdEBge0EwMCNKphwssPsFrnIr34Z9cKbgrccEv+18ChkUAEbEhOngIzlZHXsW6n7YWiUenLLxtQpjICoSh3U5Lap0H5Ni53z/s910p5oDb5wwgGxunFByxa+YKBYdzso1Zu7c07tKprVKa6Fg2PzuXYEKbbGRMe2ozMK7jLc4WQI9sANDJkVgoKjx4A0oAWWUNkZVmqPXGgFzJnqo/mcPD7gyz6JAVtp7XgKs+t3j3soZGVsa+wW3a4T3+f3EoX/+oN984w58sNyXl3hF/7gtOz7z/K6bfMOhyEquuHk370+d+Jf2wk0/Gd9q6/bHLyffcqRcysapOgNZPp95p1ncRvRrV/6dvWMDnTAFzgPSFZB1y1/IjJ07hoSCWaZyWI9f7trBnOidsRQEWFdc+z5wVBVghDMUf8xBL7OtMDkuXEdRMxdZqftSq3QASQkJIyiQ7JDWE+rBZHGs1SwpKQaVkampEdJUhqRE6VsLNCtbGVByyCl5rISu+8tT6FJCm5jJigWunXDLH8nh1ty2CVQwJJIaQsRpMA2goq4ShVs0twdb5sPUBRy3o6nSYvBEcmXVD6WYtqzEtENGv+SJStJ3LWGonfTuBAUmwk/qpn/ppNkggCSZBJIAEkFSIHwKDm7nsnVOP1/imJXPVpj5qU5ti1RCKVcOFZk3Hjrayc1Q1AEbm1obYKJnaesombVsjCskr+KPNoJIKkESAWIJmOIABMQpqKIAaqKGYmkliAGIT7GKzisUqhq8QXH3UJoxGAeDliPMjppwCmZXTpZqHFKTwBwDOAOhDRDWj2fJWgeiwEO3XxW4F141XAjAAAXsYQQIEUAtuqDY1QC2ohUVqALBbVh3jh+Gi55OjnHKwovXuQ6fM0SUAmV9DDVbwDnH1uQsW877S8GXx/TEwPIJVHjEbh8TkAShcxuwB7HWfo/Z4/xDnyAcGgW0iIPMaADhjAWZCB0RnCQ3gZsMvoZtJoQFmBHzcG6P8f0VOWx/0hSQsZsuHzwsgjBfrgPMcADDAAAIDDIAdACgFEACvPvmVBtP4Yd+pvlUCNXFUFxL8wKPDNfB9eOrV2+SD9vma+7wOfMPj+S1f53PGO1/n13zIc7eIS5mTLnDl1MG40/y03+H18T4ZTM4/H3duL5L/9KQMfQOG+AIJDi2SBzlnyEIiHlpERnWq1VDV6Jr0u/jck7XV7BAVX1799AkACaMbD7AU7LoQdzUr+GgKvLz2FTY1jOBfSUDJjn6ac77yITGpfyeyt3QGC1/5cnTf6J4WvkXFxm6c000KtqSFbwDyBIonpmqad884AKFTT0EmVS10Fxigpa9muv7hcICAxylAwIAwhAEIGBAGBAzAHC0ogk+EAQifScfcCAQMCBirAehbcWRfl89C/o+pGkMqjMfgZcbmi0Mkn8ZgVtmY8t0jZgD7DsC6TDKXQJcqVm7qdjNbTtk+iWu7YPURLCDBHLgJepcPh4syzr5suTfTXG8uZNMa2mUc/ZoGFyClgl5T4frSNgeI2rqWYG3GtvaoKEfo/aACzqbbAKIigCRVcBKdmMpm0ICRCL37I2hmsC0AWN32y6SgWSMkAQMsSIhIEkBi6YyAbwOACCy5HB4CpbuJkrBVNGIsmix5AcQsQBi8tIedTTgSHbn1EjmBtB155sQCxg0xUCAEFheanRsSaLdRFgiBQr0tmgs1Awi7Amd1cUzNmFQr1dAFKeZoqZ7ZdVJZCJHcEHgGgAwQ5ImVANQZRQI0jNYSIikAyDwAYoYlSYEhHmjFCRavoBsBcpDCLZFPGCAgAAwADmCAGRwAOJwyHhwABADEABQDBPBN4Zf8zY8aX/VS+SBmuePj3mSR50tldL2jJj3IrH/+Lc05fjX9obP5/Hxpjyu+w4MnfMXmz9/xa/6ty54vxk1RQSuW43Moo8zHftwY8/+Y/18cW/nkz3jjDL958YlOeeIz1xQXE20MWruWJuvlDEuJH2ToXemrU3zymXdZspO8+xRxFpG7XnO7kjSMygNQd62iziXEN2yeQ5uWufj4c8QolFKLLzJI3f6E+UHcf8NtcfcgE97tMlusqasQ98s3zU/NGcmrxa0I947jaJCetCQNaamGidYOyoFA6jnmD3r266Uaztp6Arj3nsy5xKi8wZ/U74cp7hWV95J9Hay7z2GeAPfrA7yQMGyT21VxVxWFipE0ZlTe2r1UvO2kDUTllIswUbOW29z3bKZhei/ug697mUd9KmS67CPAOs36KfheAL3cww6NvsRG7sptYSbAiidKgWDgIB6KfTU3ojyUFRJrHQMiFkTwxLcPjt2slzT5bYKNtS7GCnO7jW5VAIO12dq90ZuYfJ7sVAK0mqHErVQOb6Sf/dKNg+ZyrCknQEYlfVRt06Rx8EVvPB+bLaeR+WYCQBrHjYKFd5mlJgKgogWj+bcugKOxngGXwQ1pvalAAIjEoj1iEQBdW0VRB99Tm9ujLzoehOdFadxGswDKXabQOs47W6taLNRrQGGBNdWqXkZR3cxNU2Y4AMezuodMiW8UDCyA6gGgAErYAsYxpvE/uirgvB9Ci/M2TTXacKOYRpyy4CyrxKqVhAog/UQVcuLrTMk5M8U0npHDZEVg3Cb12M7ct7W2J9lDsDZOXFfbNZxnwd1sNpyNc8TtUpbuQWsbh3YdTHYsD4AfwuLmHTdYwYOVuRuSiuXW8ygvbzZP+diPG9bLTnR3kCIBdnc9oZwXCHxhiZdAgjyHeGkFKNPSZwg9tTVm1if3xgtPub6jKxjWVhyrjz6ATJ1R91MssCtbdazyhdGFp8JpBiDqZZdSVzUM09eFUklt3DU+0fQpN3YmLNI7M05udDCXepJNgjJe1bAxMb1Z8gGp+MAh9qblhnErfoT4xuveawLKbDub7LLlzVWtsznR2Szd3gdvzAD21iEjZ8SX8j7/3hhYVbJJdLa1zTWN4sR0uz4uPqG7i/GZgPd1evePnCh62drmmnb72sRqgS25fUpmJ+QI8FLkTL1sbZNHg8JEtVjdilOxvNieJafqrszzaiCxRBpOFRFyqFiGa9r6ugbtuNoKo1VqQi61gwAxrv7KRoEoTOum0I7JpTQbAFDh6oYIrSAKQhrTqzCEVOCH2UOAqO7yEu5irU5Y4epG3Naq+vRaKOlk3qnxRuPhtc8BTXdNtrnrTsEVjkJBI1vUYrubFuUMKW9s2tHsOvdDamOq9AyrNgCjlqTSqBZvuU56poPcHRe807E6q5UI0JV3GuhaWE3LWZ9C3Ztt7E0q4qKPRQM85ULZtJMLQ+uTtonKfZgnR9e95cUvi0/Fnraadz3YddqxxjNAgEhsYQ4G6Sk6AAibgCSXfbRtGpykBnacmgbXve3TyOFj/XOx1It+4K4HUof2ygQISMAQqEjeWAAQ3gScVl5kWqbRSQKCrTpc+yYOHzXQGdjrUeCFgZgDXekjgJXFCSkajMxKAFDKhOMReApjkzRbD5Casuvf4ePaQ71J/Az2Q7zvIzYHO+nLLFwUhgqYrTJpAZ80UYAgDuntiFLaQiwgGle/08ND7PUTc1tn6RXffwfen7Oqz0+fAojAYIYCIgEEAORw5yQR5xO6q9/y4spZ646LVyS3fYrVA+9PdOhYaXsGBXwHzbj6TT8MFFahsNp6jnUfXvcDdMbL2XZf4qvfdrza7QHx7ImHKKONk7njRdHltm5frq5eBgBkQpfG6608g7vrFgt1Am7W6ltbxdGg3hITBQKPl0qbCxujP9Ullglb2lxYKC2M9qxNs0z42tJEvbh5L3kURPNqq8vtk6SyPb1dZOV70OCfP7aR5NGWT9a3SW1hqVubSrqDO/8IAFLIn20/CdTHSbVMaNx7dQ6kcEuNtp8FNjZIvUyQ3gp97raapmWpRlqXWXd5dntJHXSnNS1olO/nb8en31rGfYOTKj5rn3yJTmHHdt55nZpapAmpJHp5YxGA4L92Vji4196mYrvtFenNtlggwoLbbB/4oZLYknUVfF/iZnvUT2Xdk1fqO7cBoqec+2NsN2PveW35yajldPnMsc/zG5SYgZTFI2qFAQY/BREI0z0EfB/h61OrmuZswyNq98lTCE4/TIaJOeR8h+xO32Mai+uSCEdgKWwMoUeE2pgXfCSbvPY2sj+ClmQdWSocYH3w4GSdIvFGhQOR/XQkXJdPPAO224sBL4Fk5D6SEZIqVmAAmGi0yrgUspHhUp1FZjsJHKo4ZTsTskQ7OQ/EOPikrktyryPlATkceAngc1IP9/KVdAipZUjIcVbg87YZ/HrofujbpgUACyGnsk2N3TelHzOg9+0gVSbbvkN2yyeSQSolyVO8IOwNA50rhsXFgJdAbPE4CuFuZktsqZ59DAMBEJWBAbVHMECHk+AtEXcWYABBcF9NhaZCyFfGP9DrTYeFAacfiQcL0G4xWSUAOI2ACmYoJEwKRcXCRCJ1tD4JOOJ51HzFjlLf2Je2geBRnT20KC8zHJMtdS584qYEpuar4g9OqQcsWZ5L1i4fZcb1ud1NAyX9uORARABaaELLQKCTzAGCgAZTRsAMAnQEIwr4CsVLXY69elweSribcfZ9aC3uZgBgXKdfItvlMXuqEdfyl4Rb9cub1Xv8n4/+g//sjWbTk1GoLxjfUL9k+gWzys5O3//jXgK+bnyn+qHyrbZ9/ySDzTX7wr3T5vIXJAGPBpSpp3MUq7mX31TjzvhX9B3653aZwcFFTJXrF6L0Ltw2B9N34WlUlVbDq9znYPI4lScEGjYFJ8tivdiO5PESmyLJNMmeaUPnqj2247giulSkeuBFoIdZjCttroeurJt2acT28wBTtk0zROdlO3REimx6NRqQeEJj4DVd6r3/IDShXBr6nOt8cq6qckB8xCaAGAKIIYBYJEVo+jQlqoVpWKaje8KW5UE25QMPi6+Imzp5AxW3vI4zApDFkso0jrbvbRa9H9BvRyDGXEXP2/Fk6gIv9no6E6gBaKo02EyARtrKICmYgUbKZqCRsg+QcJilpL/yYbmq7Ui9kkLFVEMtaiimGuqnfmoDTQGB1eCaiTyIFYMoNtZF2VmqOKr4BE4KNBMA2MmH0OzsgoIiWIkhqz0ANcsFP6dwzUKvaPbFSWCdSWCdySUAtaihFjXQQBLA1xMJVBajX4yyAGUxxJhpGOYMjWAzDVtIDSAAU6bwT6EX+7+Rq5nTMCyK6p9QRjV7DytoHWCS9VNXjluhSm5izRDmtyN+HcnDBAYEAoCMPUAWkDa3elNIrmhfOH76az7CuTEPnxvzjHMzrCp2tZhqUUMNBRRQwBDYJAAw4k9wroLbD28nI5Gmcj7EqqmWAMimLOfDVPRuqZIzTb3QVDlvi+kIUzdx045jiQudk7O9WmENyiXgBef8GCpA3xgWN+6uLcSUy93y77+ex3/OGuZ1Dc12Ug6GQAwAB2tYkCeG8ZG9ArxhzUjJKQvaiDtS1G7cbhdrUm1HBSYnvfPXx49J3FqR1nbU2VzUymePTLKyQAxxrL24I8Wot1DAzXW52zkA7bKSd+xEUSy0PuPDbqlA3/0lca6TncUiKHnIuA1EXz+YfEyEJxf7bNy7fRhqQl5Tyuz+Gq/uYuRDX51z0LjLjiHifJJM7SZGeWqKXRcTSHySVEh5X0iHRK1QLaJWZdK46wVEkbqcXHA6uaD1ugZTv0Gr1XKvFiuva5MLiG4Y5sIMhtm5Uhqotx1GqtHLP0Y11plnoOkuu90Rov9faFq4pv7UxvK2c0lVb/sYpa1xQrtnyvlhVs6PSWO2qGGoSvsSiU2eoOQHOjh9berPfl9tHz8zMRDrzsU9f7Gwv7HNnDvzENj5vzCMF9OhOoRek72p/SkqRj/5fS6Brjfz6/uTeUvy2tLZ/5oOdgkiezu3tMmfTp1edgh9CeEiF2lIpUplp5IYgH7ldzudMqdqUUVxcKWiNtht8t2hsvHY0MToz+Z3GAn+veHyn/1XNjiF7d0jB4z5f8z/Y0Dc3bF4zsT5RBt8oVGeKtNwMVESm+xmXLUbrnpDePnPmrTId9GdVZeTgnz8cxRqwJ/o6uyye8Xxeb/ic95cd3Y5jMb1QhqOQKsTSg7BvnIX30XppYXtOF82xLsr2rRF679ZW0/XjnuIiA6qbSoiavVCtcbraaBTD4Uo/qKkVezYstNotbw/a2+rtiuWF24cgB9Jl4PqbVb3dl1ixyZ/OnUF2ZwIE80q6RRSKAGQ9FzCKsm8JZlC+s/tzcO58GTH67XrI4hImJiRu0rTU154HsAxzL7nda8dYfKUzx4CI7WaJjaDoJnvlRQ/QHyofE4XmfXVcX1vkLp086v87pmLbrlHKrvwgP0oGYwbLBV63WTfaP2arBOtB57Qlmh8jEXzN4sjFKuvbJJqQzcXVNPqbiYAHKa1tWu2d2ma0zz9SJpmUOpwux7HlE3NOtXlIn05YVnr5XbCsOyTBNK6EufETSHHwRA5CqxCpMAGubAhahrBzdTuoYqKSC+klr5qYRwRpIZSbuDdsbcQZeskTgDci28bKmwUO1Uc/dzI7qLwmjmPuTTL0RwLHPgIbl56bqMKUOo+h79lWF1BKbzsDBy2QwH9ZHPBoj08uuQIG+eWuokCCqYcd9rdSMc3DJDGWQcAEcAXRYEAOABgDcDhVAMIgDo3DTZq7XDDPEo81B6//o+9+XEA8Oav++VaC56yaKcZN03XPsgM33DN1zR/9I2Tv/XffrJtMjx1wkvcNkk9CPJRn/Fhb57g47zCLVleiT/wN1659uSTAeDTb0gAXvH/zP/XyXv8/6J6pye06haV9pEFt2c/1rrY8VxbnjQ/JUnqRJKcSXLuHSghvnIx9hqFM3iufR0nwVO2/XKSynRiy3TG7+0k7ZXrKFOWAQJ+nqZ6CVLbROwXW4ObdKj5xNTW01ENNTHqajBm8n6DawYdwohHUfIYXq30PoDTqu6SABJEAkgAsWgHRt+PAXvrK5isyg/TAkDaomK/Ex+J065kBEvJtLeWELhujsTPm3TzGEYYpxyD3JFGsYRVGEETwc12zI4a3kCocyc+tbJOk/HiOZnJgx1NnuCZwHaC9XiwpkJToQF6eYwWy5wxRGUKz69YETBbhoBVnIgZp8zWe1I6ahZWsXGwQi5MQQAwocHd6pP5y1v5i5BPw9pqguA5JmMcE5jALux1Ap3TcwGJcdq9puiO0QektiZW8Eg8g++RvTH5SwWo9oaOLrzv+YEhhbt9cgRYD4CUgQOMaACY2CEhJB9xTPlxFHgMNY8B7wPsVmMLeFTTyEBo8NSHmIJ7FPKb7wOAaZrpcYEAAD89WFG4jt2k3+O/c2rxya/s8GGf5fM9efPjbkzvfWUvzn8GbkuxeXXWCoBX4pPxLUfhLihv3YuJgJ+aWIHipyRe8b2sgBj6W+krUwEDAoblC+oJW96EF5GD9V1iie9VD4TQqqdy1JZnfZ/dqoYBYMscMAZ9sK0vJ3G+zzJ+3BjD7VGsOVwmRwiYbGOG2XnRxkdOimU7Fhlp9VMkr6yopFAHZ5GkzPSOgmvlRAzrGi0sakU8eSR/ZH5fzeTVpYRQ1Ps6x0ft2LNIfMQmPmITixhiiCWmt7mxbTaVMm+U1UNuJh0OZbN9kdqjwGbaPfhs7Cq/9nFiG23lKJVAogFWN4Pz1JCuqA52LzRPuzokBdaYsosJaNZ9UB09YkyRAgoooICCpkJTEFAs/j5WwV+wbhRmj3VzGOYqIuWsjmwN2dq5VZvWH24N7NpauoHTXNvsUMM8uuDbw/TvA45+SANCZ/s8+EPs5ndR1utGyzgD8Kh2CCCAAAICCCCApEByhoAAAgIICCAgIH7ixxYA5aZe7cc9Xq4BUAIq6dXUZLIWZNsWNdfJeR1rg8VkRRPbilaKlWrrOuegyjAfhiqn9g5D9NGHbB83SO2PDZ2PDGtZYIlIEkQiBSQAsIgdTMNf8FcANdRQADXUpha1qEVDaeqsgr+QQiVoHQHA5eVm+dF6DS7v+RDVOE/KQg3rEbc4904PTdaiKn0PYPNzlYV5oWJHuUwqqdasszt8NizJ0ASAhYXs5J0Toiqb5sYCIAQ832rkRCXdmwAQU6H7nfKIHR1uGMaQu61WhS0lazeBbtKWshw9d7tfAPjSNIv0M++LGsawGG4wlSl2ahzU4q1AeRLFrqBZgsrKkgOq3tqAS5k8YMYpmw885sPP06/qnvBszggaAYuMs+jXv5UAK4DXt+8lU2guJrBUhysJ8kK0hQzPOIed/t67Cnf+6To0uJzHXvKIx94dnscmG+evuc7X7EPOvX3N1ofLiShmePxdDkh/5St2+PUzb75TJovL+VsxEK4ciCfwa4DHgQgOHTMsOp756uQpRwB5yzH/vcR1my6GiuUBQmzbPY7g9nFy19upkSDELhZH0h9s/WxJbhculrT0J+uhWq3HWO5rmDPkXE5uud09v+K99pCKAefq9NaEexXkTz7eVj7xBbPvGUqrh5ylvpvUTasaypoYCSPAcmPOkgCIeymUbUkDyqEoFMnoaungzhvtYD4A/RgFG9/vdrc5RB5i1/6wY+GzZHFry72i5X2pgcZjWhp/2p8sPAQzHNw5oyJWohXLDSlFOep08++/cut5S6JEOHgUq2L9wpXKrU9j/0cKMv/NvPs1rG5nHZrTcPmXHBoYuXgEkLcc859r3vls3pHEUUvZ3Y4ezSFTBHa9BgnmLvlAfYzym23yK863c1M8q7zVpIVrorqZft4Z1WVLcq+bSiXrpDZYjX8tdrxC7RlKfCuFX3SxuPjl+NcKWVc8AowcNz4Z/yoD2DeGg3WuCp8c4GnY/Ee2jSiPEtBj4Obf/YYBxvx/DnwBAA==) ## QNN HTP Precision QNN HTP supports running graphs having a mix of floating-point and fixed-point data types. QNN HTP can support running float32 graphs using float16 math on select Qualcomm SoCs. The client is expected to set up the QNN graph with float32 tensors and QNN HTP accelerator will finalize and execute the QNN graph using float16 math. Note QNN\_HTP\_GRAPH\_CONFIG\_OPTION\_PRECISION is deprecated starting from 2.35 release. If you are using and SDK version >=2.35, there is no need to set this option. QNN HTP backend will convert user provided float32 inputs in [QnnGraph\_execute()](https://docs.qualcomm.com/doc/80-63442-50/topic/function_QnnGraph_8h_1a3ea05f42a9295f9a74a2e3a0cdd64228.html#exhale-function-qnngraph-8h-1a3ea05f42a9295f9a74a2e3a0cdd64228) to float16 and execute the graph with float16 math. The final output is provided to user as float32 outputs. Note Please note that, float32 math is not supported by QNN HTP. ## QNN HTP FP16 output difference between SM8550 and SM8650 The outputs of floating point models on HTP backend will be slightly different between SM8550 and SM8650. This may lead to slight accuracy difference between these two although one is not more accurate than the other. This is because of the changes in the hardware which changed the associativity of some of the computations to achieve higher efficiency. Note This same point can also be found in 2.9.1 release notes on the “HTP Float16” slide. ## QNN HTP Deep Learning Bandwidth Compression (DLBC) Deep Learning Bandwidth Compression is a feature that allows inputs to be compressed so the processing bandwidth can be lowered. QNN HTP provides a configuration option for users to turn ON or OFF DLBC through client usage like below: 1 QnnHtpGraph_CustomConfig_t customConfig; 2 customConfig.option = QNN_HTP_GRAPH_CONFIG_OPTION_OPTIMIZATION; 3 customConfig.optimizationOption.type = QNN_HTP_GRAPH_OPTIMIZATION_TYPE_ENABLE_DLBC; 4 customConfig.optimizationOption.floatValue = 1.0; // set to 0 to turn off 5 6 QnnGraph_Config_t graphConfig; 7 graphConfig.option = QNN_GRAPH_CONFIG_OPTION_CUSTOM; 8 graphConfig.customConfig = &customConfig; 9 10 const QnnGraph_Config_t* pGraphConfig[] = {&graphConfig, NULL}; Copy to clipboard For offline preparation with DLBC, the backend-specific config file should specify the following option along with any other desired options: { "graphs": [ { "vtcm_mb": ..., "graph_names": [...], "dlbc": 1 // set to 1 to turn on ... } ], "devices": [ { ... ... } ] } Copy to clipboard Value of 0 will turn OFF the feature and any positive floating point value greater than or equal to 1.0 will turn ON the feature. By default DLBC will be in disabled state i.e. when configuration option is not provided. DLBC allows weight data to be compressed to lower processing bandwidth. QNN HTP provides a configuration option for clients to turn enable or disable DLBC weights. 1 QnnHtpGraph_CustomConfig_t customConfig; 2 customConfig.option = QNN_HTP_GRAPH_CONFIG_OPTION_OPTIMIZATION; 3 customConfig.optimizationOption.type = QNN_HTP_GRAPH_OPTIMIZATION_TYPE_ENABLE_DLBC_WEIGHTS; 4 customConfig.optimizationOption.floatValue = 1.0; // set to 0 to turn off 5 6 QnnGraph_Config_t graphConfig; 7 graphConfig.option = QNN_GRAPH_CONFIG_OPTION_CUSTOM; 8 graphConfig.customConfig = &customConfig; 9 10 const QnnGraph_Config_t* pGraphConfig[] = {&graphConfig, NULL}; Copy to clipboard For offline preparation with DLBC, the backend-specific configuration should specify the `dlbc_weights` option along with any other options. - 0 – Disables DLBC weights; default when option is not provided - >= 1 – Enables DLBC weights { "graphs": [ { "vtcm_mb":..., "graph_names":[...], "dlbc_weights": 1 // set to 0 to turn off, dlbc for weights ... } ], "devices": [ { ... ... } ] } Copy to clipboard Compression for inputs and weights can be independently set. ### Limitations - Only supported for offline preparation. - Not supported with weight sharing. - Not supported with spillfill buffer sharing. - The number of graphs supported depends on whether compression is enabled for inputs and weights; graphs supported: - 32 – Input OR weight compression - 16 – Input AND weight compression Note The DLBC Weights with Weight Sharing feature is not supported. Starting with release 2.36, creating binaries with both DLBC Weights and Weight Sharing enabled will not be supported. Any binaries prepared before 2.36 with both features enabled will have DLBC WTS turned off regardless of the settings. To use DLBC WTS with such binaries, re-prepare without Weight Sharing. ## QNN HTP - Setting Number of HVX Threads This option allows user to set number of HVX thread(s) for a particular graph. The inference time depends on the number of HVX threads utilized. If more threads are used, the execution time of a graph will be lower (i.e. faster). Number of HVX threads can be configured for both online and offline prepare cases. The value passed in the config during binary blob creation is what gets written in the serialized blob. Number of HVX threads can be re-configured by passing a new config to `QnnGraph_setConfig` QNN API. It is important to note that number of threads **can not** be configured/re-configured after the first execution of that particular graph; it has to be prior to it. Users can set the custom option as such: 1 QnnHtpGraph_CustomConfig_t customConfig; 2 customConfig.option = QNN_HTP_GRAPH_CONFIG_OPTION_NUM_HVX_THREADS; 3 customConfig.numHvxThreads = 3; // set a number. MAX = number of HVX HW blocks for that SoC 4 5 QnnGraph_Config_t graphConfig; 6 graphConfig.option = QNN_GRAPH_CONFIG_OPTION_CUSTOM; 7 graphConfig.customConfig = &customConfig; 8 9 const QnnGraph_Config_t* pGraphConfig[] = {&graphConfig, NULL}; Copy to clipboard The backend-specific config file should specify the following option along with any other desired options. In the case of offline prepare, If “hvx\_threads” option is not provided, a default value of 4 is written to the binary blob. In the case of online prepare, if a config does not set any number of hvx thread(s), max supported value for that SoC is used during an inference. Config can be used to set number of HVX threads as such: { "graphs": [ { "vtcm_mb":..., "graph_names":[...], "hvx_threads":3 // set a number. MAX = number of HVX HW blocks for that SoC ... } ], "devices": [ { ... ... } ] } Copy to clipboard ## QNN HTP - Enabling the system level cache allocator This option allows user to enable the usage of the System Level Cache Allocator for a given graph. It will help the by saving overall bandwith on the use case. Users can set the custom option as such: 1 QnnHtpGraph_CustomConfig_t customConfig; 2 customConfig.option = QNN_HTP_GRAPH_CONFIG_OPTION_OPTIMIZATION; 3 customConfig.optimizationOption.type = QNN_HTP_GRAPH_OPTIMIZATION_TYPE_ENABLE_SLC_ALLOCATOR; 4 customConfig.optimizationOption.floatValue = 1; 5 6 QnnGraph_Config_t graphConfig; 7 graphConfig.option = QNN_GRAPH_CONFIG_OPTION_CUSTOM; 8 graphConfig.customConfig = &customConfig; 9 10 const QnnGraph_Config_t* pGraphConfig[] = {&graphConfig, NULL}; Copy to clipboard The feature is only supported by specific SOCs. By default the option is turned off Config can be used to set the option as such: { "graphs": [ { "vtcm_mb":..., "graph_names":[...], "slc_alloc_enable":1 ... } ], "devices": [ { "soc_id": 69, //representing the soc ... ... } ] } Copy to clipboard Note This option can be configured for offline prepare cases. This option can’t be modified during inference. However, it is possible to force the disablement of the feature during execution Users can set the custom option as such: 1 QnnHtpGraph_CustomConfig_t customConfig; 2 customConfig.option = QNN_HTP_GRAPH_OPTIMIZATION_TYPE_ENABLE_SLC_ALLOCATOR; 3 customConfig.optimizationOption.floatValue = 0; 4 5 QnnGraph_Config_t graphConfig; 6 graphConfig.option = QNN_GRAPH_CONFIG_OPTION_CUSTOM; 7 graphConfig.customConfig = &customConfig; 8 9 const QnnGraph_Config_t* pGraphConfig[] = {&graphConfig, NULL}; Copy to clipboard Note To restore the previous state, the same sequence should be called with 1 as a value. ## QNN HTP Backend Extensions The qnn-net-run utility is backend agnostic, meaning it can only use generic QNN APIs. The backend extension feature facilitates usage of the backend specific APIs, namely custom configurations. More documentation on backend extensions can be found under [qnn-net-run](https://docs.qualcomm.com/doc/80-63442-50/topic/tools.html#qnn-net-run). Note that the scope of QNN backend extensions is limited to qnn-net-run and qnn-context-binary-generator. HTP Backend Extensions is an interface to provide custom options to HTP Backend. It is also required to enable different performance modes. These options and performance modes can be exercised by providing an extension shared library `libQnnHtpNetRunExtensions.so` and a config file, if necessary. To use backend extension related parameters with qnn-net-run, use `--config_file` argument and give path to JSON file. $ qnn-net-run --model \ --backend /libQnnHtp.so \ --output_dir \ --input_list --config_file Copy to clipboard The above config file with minimum parameters to use backend extensions config is shown below: { "backend_extensions" : { "shared_library_path" : "path_to_shared_library", // give path to shared extensions library (.so) "config_file_path" : "path_to_config_file" // give path to backend config } } Copy to clipboard Users can set the custom options and different performance modes to HTP Backend through the backend config. The various options available in the config are shown below: { "type": "object", "properties": { "graphs": { "type": "array", "items": { "type": "object", "properties": { // Corresponds to the graph name provided to QnnGraph_create // Used by qnn-net-run during online prepare and qnn-context-binary-generator uses it during offline preparation "graph_names": {"type": "array", "items": {"type": "string"}}, // Provides performance infrastructure configuration options that are memory specific [optional] // Used by qnn-net-run during online prepare and qnn-context-binary-generator uses it during offline preparation // To use a device's maximum VTCM amount, set the value to 0 (QNN_HTP_GRAPH_CONFIG_OPTION_MAX) // and specify the target SoC through the device config. "vtcm_mb": {"type": "integer"}, // Corresponds to the number of HVX threads to use for a particular graph during an inference. // Used by qnn-net-run during online prepare and qnn-context-binary-generator uses it during offline preparation "hvx_threads": {"type": "integer"}, // Set Graph optimization value in range 1 to 3 [optional] [default: 2] // 1 = Faster preparation time, less optimal graph, 2 = Longer preparation time, more optimal graph // 3 = Longest preparation time, most likely even more optimal graph // Used by qnn-net-run during online prepare and qnn-context-binary-generator uses it during offline preparation "O": {"type": "number", "multipleOf": 1}, // Provide deep learning bandwidth compression value 0 or 1 [optional] [default: 0] // Used by qnn-net-run during online prepare and qnn-context-binary-generator uses it during offline preparation "dlbc": {"type": "number", "multipleOf": 1}, // Specifies whether to enable weights packing [optional] [default: false] // Used by qnn-net-run during online prepare and qnn-context-binary-generator uses it during offline preparation "weights_packing": {"type": "boolean"}, // Specifies the number of cores the graph will use for execution [optional] [default: 1] // Used by qnn-context-binary-generator during offline preparation "num_cores": {"type": "integer"}, // Specifies whether to configure short depth convolution for the graph [optional] [default: false] // Used by qnn-net-run during online prepare and qnn-context-binary-generator uses it during offline preparation "short_depth_conv_on_hmx_off": {"type": "boolean"}, // Specifies whether to configure fold relu activation for the graph [optional] [default: false] // Used by qnn-net-run during online prepare and qnn-context-binary-generator uses it during offline preparation "fold_relu_activation_into_conv_off": {"type": "boolean"} } } }, "devices": { "type": "array", "items": { "type": "object", "properties": { // Selection of the device [optional] [default: 0] // Used by qnn-net-run "device_id": {"type": "integer"}, // Select the core [optional] [default: 0] // Used by qnn-net-run to select among the cores available in a device "core_id": {"type": "array", "items": {"type": "integer"}}, // Select the available core type [optional] [default: 0] // Used by qnn-net-run, 0 - NSP, 1 - HPASS "core_type": {"type": "integer"}, // Selection of the SoC [optional] [default: 0] // Used by qnn-net-run and qnn-context-binary-generator "soc_id": {"type": "integer"}, // Selection of the SoC model [optional] [default: 0] // Used by qnn-net-run and qnn-context-binary-generator "soc_model": {"type": "integer"}, // Set dsp architecture value [optional] [default: NONE] // Used by qnn-net-run and qnn-context-binary-generator "dsp_arch": {"type": "string"}, // Specifies the user pd attribute [optional] [default: "unsigned"] // Used by qnn-net-run and qnn-context-binary-generator "pd_session": {"type": "string"}, // Used for linting profiling level [optional] [default: not set] // Used by qnn-net-run and qnn-context-binary-generator "profiling_level": {"type": "string"}, // Specifies whether to use null context or not. true means using a unique power context id, and false means using null context. // NOTE: This parameter is not supported for v68 onwards // Used by qnn-net-run "use_client_context": {"type": "boolean"}, "cores": { "type": "array", "items": { "type": "object", "properties": { // Provide performance profile [optional] [default: "high_performance"] // Used by qnn-net-run // Note: This perf profile will be overridden by any profiles specified via the command line option --perf-profile "perf_profile": {"type": "string"}, // Rpc control latency value in micro second [optional] [default: 100us] // Used by qnn-net-run "rpc_control_latency": {"type": "integer"}, // Rpc polling time value in micro second [optional] // [default: 9999 us for burst, high_performance & sustained_high_performance, 0 us for other perf profiles] // Used by qnn-net-run "rpc_polling_time": {"type": "integer"}, // Hmx timeout value in micro second [optional] [default: 300000us] // Used by qnn-net-run "hmx_timeout_us": {"type": "integer"}, // Adaptive polling time value in micro second [optional] [default: 0 us] // Used by qnn-net-run "adaptive_polling_time": {"type": "integer"} } } } } } }, "context": { "type": "object", "properties": { // Used for enabling Weight Sharing [optional] [default: false] // Used by qnn-context-binary-generator during offline preparation "weight_sharing_enabled": {"type": "boolean"}, // Used to associate max spill-fill buffer size across multiple contexts within a group [optional] [default: Not Set] // Used by qnn-net-run and qnn-throughput-net-run during offline preparation. group_id value must be set to 0 for this option to be used. "max_spill_fill_buffer_for_group": {"type": "integer"}, // Specifies the group id to which contexts can be associated [optional] [default: None] // Used by qnn-net-run and qnn-throughput-net-run during offline preparation. "group_id": {"type": "integer"}, // Used to set read memory budget size in Mb [optional] [default: 0] // Used by qnn-net-run and qnn-throughput-net-run when using a serialized binary for graph preparation. "file_read_memory_budget_in_mb": {"type": "integer"} // Used to enable I/O memory estimation [optional] [default: false] // Used by qnn-net-run and qnn-throughput-net-run when creating context from a serialized context binary. "io_memory_estimation": {"type": "boolean"} // Used to enable init acceleration [optional] [default: false] // Used by qnn-net-run and qnn-throughput-net-run when creating context from a serialized context binary. "init_acceleration": {"type": "boolean"} // Used for enabling Lora Weight Sharing [optional] [default: false] // Used by qnn-context-binary-generator during offline preparation "lora_weight_sharing": {"type": "boolean"} } }, "groupContext": { "type": "object", "properties": { // Used to enable shared resources across different contexts [optional] [default: false] // Used by qnn-net-run and qnn-throughput-net-run when creating multiple contexts from a list of serialized context binaries. "share_resources": { "type": "boolean"} } }, "memory": { "type": "object", "properties": { // Use multi-tensor shared buffers for input/output [optional] [default: QNN_HTP_MEM_UNDEFINED], Refer QnnHtpMem_Type_t // Used by qnn-net-run and qnn-throughput-net-run "mem_type": {"type": "string", "enum": ["shared_buffer"] } } } } } Copy to clipboard Note 1. soc\_id parameter will be deprecated, For setting the Soc use soc\_model parameter. 2. [Qnn\_SocModel\_t](https://docs.qualcomm.com/doc/80-63442-50/topic/enum_QnnTypes_8h_1aea3262897ad2f237f9e5189419ad2214.html#exhale-enum-qnntypes-8h-1aea3262897ad2f237f9e5189419ad2214) will be deprecated, For setting Soc Model refer to the [Supported Snapdragon Devices](https://docs.qualcomm.com/doc/80-63442-50/topic/overview.html#supported-snapdragon-devices) 3. fp16\_relaxed\_precision is deprecated starting from 2.35.0 release. Moving forward, there is no need to set this parameter for fp functionality and it will be determined based on SoC support. Backend extensions performance modes can be enabled using `perf_profile` parameter through backend config as shown above. Valid settings are low\_balanced, balanced, high\_performance, sustained\_high\_performance, burst, low\_power\_saver, power\_saver, high\_power\_saver, extreme\_power\_saver and system\_settings. Note that these performance modes are user defined and customers can choose to define their own performance modes according to their needs using [QNN APIs](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-performance-infrastructure-api). These performance modes use different configurations of core clocks, bus clocks, DCVS participation algorithms and sleep latencies. There are 3 types of voltage corners defined as TURBO, NOM and SVS which further have different voltage levels. Apart from these, there are MAX and MIN voltage corners which sets the frequency to maximum and minimum frequency supported on target. For further details on the performance modes configuration and parameter details, refer **hexagon sdk** documentation. These settings used by different performance modes defined above are shown in table below: | | **BURST** | **SUSTAINED\_HIGH\_PERFORMANCE** | **HIGH\_PERFORMANCE** | **BALANCED** | **LOW\_BALANCED** | **HIGH\_POWER\_SAVER** | **POWER\_SAVER** | **LOW\_POWER\_SAVER** | **EXTREME\_POWER\_SAVER** | **RELAXED\_POWER\_STATE\*** | **RELEASED\_POWER\_STATE\*** | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `sleepLatency` | 40 us | 100 us | 100 us | 1000 us | 1000 us | 1000 us | 1000 us | 1000 us | 1000 us | 2000 us | 65535 us | | `dcvsEnable` | False | False | False | False | False | False | False | False | False | True | True | | `RPC Polling` | ON | ON | ON | OFF | OFF | OFF | OFF | OFF | OFF | OFF | OFF | | `busVCornerMin` | MAX\_VOLTAGE\_CORNER | TURBO | TURBO | NOM\_PLUS | NOM | SVS\_PLUS | SVS | SVS2 | DISABLE | SVS2 | MIN\_VOLTAGE\_CORNER | | `busVCornerTarget` | MAX\_VOLTAGE\_CORNER | TURBO | TURBO | NOM\_PLUS | NOM | SVS\_PLUS | SVS | SVS2 | DISABLE | SVS | MIN\_VOLTAGE\_CORNER | | `busVCornerMax` | MAX\_VOLTAGE\_CORNER | TURBO | TURBO | NOM\_PLUS | NOM | SVS\_PLUS | SVS | SVS2 | DISABLE | SVS | MIN\_VOLTAGE\_CORNER | | `coreVCornerMin` | MAX\_VOLTAGE\_CORNER | TURBO | TURBO | NOM\_PLUS | NOM | SVS\_PLUS | SVS | SVS2 | DISABLE | SVS2 | MIN\_VOLTAGE\_CORNER | | `coreVCornerTarget` | MAX\_VOLTAGE\_CORNER | TURBO | TURBO | NOM\_PLUS | NOM | SVS\_PLUS | SVS | SVS2 | DISABLE | SVS | MIN\_VOLTAGE\_CORNER | | `coreVCornerMax` | MAX\_VOLTAGE\_CORNER | TURBO | TURBO | NOM\_PLUS | NOM | SVS\_PLUS | SVS | SVS2 | DISABLE | SVS | MIN\_VOLTAGE\_CORNER | Note RELAXED\_POWER\_STATE and RELEASED\_POWER\_STATE are internally applied based on performance profile to lower the votes. These are not configurable to user. Above table is ordered from highest performance (BURST) to lowest performance (EXTREME\_POWER\_SAVER). BURST and SUSTAINED\_HIGH\_PERFORMANCE uses a timer during execution which helps in keeping the vote high for all inferences and avoids subsequent up-down of perf votes until timeout. They have low sleep latency, RPC polling is enabled and DCVS is disabled during execution. Note that DCVS if enabled can both increase and decrease the core/bus clock speeds while min\_corner and max\_corner votes are used as lower and upper limit thresholds for DCVS. BURST has the highest frequency and it sustains high voting, which gives the best performance. HIGH\_PERFORMANCE mode however do not sustain votes during multiple inferences instead it moves to idle state RELAXED\_POWER\_STATE in between inferences which reduces CPU power consumption. POWER\_SAVER, LOW\_POWER\_SAVER and HIGH\_POWER\_SAVER have low frequencies, high sleep latencies and moves to idle state RELEASED\_POWER\_STATE in between inferences. EXTREME\_POWER\_SAVER is the lowest performing performance mode and saves the highest power. There are 3 stages to graph execution i.e INIT, INFERENCE and DE-INIT. Above defined performance modes will be applied to graph before each stage i.e INIT, INFERENCE and DE-INIT as well. After each stage completion, the lower votes will be applied i.e RELAXED\_POWER\_STATE or RELEASED\_POWER\_STATE according to the performance mode selected by the user. Below config can be used to set HTP performance profile and rpc polling time: { ... "devices": [{ ... "cores":[{ "perf_profile": "burst", // use this to set any of the above performance profile "rpc_polling_time": 9999, // use this to set rpc polling, ranges 0-9999 us "rpc_control_latency": 100 // use to set rpc control latency }] }] } Copy to clipboard ## QNN HTP Profiling **Basic Profiling** Basic profiling report for execution provides the graph inference summary on both - Host and Accelerator. [HTP Execute Basic Profiling Events](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-basic-profiling-events-showcase-figure) diagram illustrates the basic HTP execute profiling events and how they are measured during the inference. **HTP Execute Basic Profiling Events** ![QNN HTP Execute Basic Profiling Events](data:image/png;base64,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) **DSP heap profiling** is available for `QnnContext_createFromBinary` use-cases for monitoring total memory use. Currently, a total DSP heap usage metric can be retrieved for following scenarios: > > > - before any contexts are created (when creating the first context), > - after all contexts are freed (when freeing the last context). > > > > Enabling DSP heap usage profiling for a given context can be achieved by passing the following > configuration to the relevant `QnnContext_createFromBinary` call: > > > 1QnnHtpContext_CustomConfig_t customConfig; > 2customConfig.option = QNN_HTP_CONTEXT_CONFIG_OPTION_DSP_MEMORY_PROFILING_ENABLED; > 3customConfig.dspMemoryProfilingEnabled = true; // set to false to disable DSP heap profiling > 4 > 5QnnContext_Config_t contextConfig; > 6contextConfig.option = QNN_CONTEXT_CONFIG_OPTION_CUSTOM; > 7contextConfig.customConfig = &customConfig; > 8 > 9const QnnContext_Config_t* pDspMemProfilingContextConfig[] = {&contextConfig, NULL}; > Copy to clipboard > > > **Total DSP heap usage before any contexts are created:** > If the aforementioned configuration is enabled for the first context to be created in the > relevant `QnnContext_createFromBinary` call, the total DSP heap usage value can be retrieved from the > `DSP:before_context_created` event defined in the `Qnn_ProfileHandle_t` instance, as shown below: > > > 1Qnn_ProfileHandle_t profileHandle; > 2QnnProfile_create(QNN_PROFILE_LEVEL_BASIC, &profileHandle); > 3 > 4QnnContext_createFromBinary(..., pDspMemProfilingContextConfig, ..., &contextHandle1, &profileHandle); // first context creation > 5QnnContext_createFromBinary(..., &contextHandle2, ...); > 6QnnContext_createFromBinary(..., pDspMemProfilingContextConfig, ..., &contextHandle3, ...); > 7 > 8const QnnProfile_EventId_t* events; > 9uint32_t numEvents; > 10QnnProfile_getEvents(profileHandle, &events, &numEvents); > 11 > 12for (uint32_t i = 0u; i < numEvents; ++i) { > 13 QnnProfile_EventData_t eventData; > 14 QnnProfile_getEventData(events[i], &eventData); > 15 if (strcmp(eventData.identifier, "DSP:before_context_created") == 0) { > 16 uint64_t totalDspHeapUsageBeforeContextCreated = eventData.value; > 17 } > 18} > Copy to clipboard > > > **Total DSP heap usage after all contexts are freed:** > If the aforementioned configuration was enabled for the last context to be freed either in the > relevant `QnnContext_createFromBinary` call or later on with the use of `QnnContext_setConfig`, > the total DSP heap usage value can be retrieved from the `DSP:after_context_freed` event defined > in the `Qnn_ProfileHandle_t` instance, as shown below: > > > 1QnnContext_free(&contextHandle1, ...); > 2QnnContext_free(&contextHandle2, ...); > 3QnnContext_free(&contextHandle3, &profileHandle); // last context free, config was enabled for contextHandle3 during QnnContext_createFromBinary > 4 > 5const QnnProfile_EventId_t* events; > 6uint32_t numEvents; > 7QnnProfile_getEvents(profileHandle, &events, &numEvents); > 8 > 9for (uint32_t i = 0u; i < numEvents; ++i) { > 10 QnnProfile_EventData_t eventData; > 11 QnnProfile_getEventData(events[i], &eventData); > 12 if (strcmp(eventData.identifier, "DSP:after_context_freed") == 0) { > 13 uint64_t dspHeapUsageAfterContextFreed = eventData.value; > 14 } > 15} > Copy to clipboard > > > Note > > > Please note that in case the configuration was not enabled for the given context, `QnnContext_free` will not output the DSP heap usage metric. Note DSP heap profiling feature has the following requirements and limitations: Requirements: 1. Profiling should be enabled both for the first context to be created and the last context to be freed. Limitations: 1. Only supported on Android and QNX platforms. 2. By enabling this feature initialization and cleanup time might be impacted. **Detailed and Linting Profiling** Detailed profiling report provides per op profiling result by cycle counts instead of time in microsecs. There is no direct conversion method from cycle count to microsecs because of the parallelized execution of Ops. Hence it is recommended to use the per layer cycle timings as a reference to compare/measure the relative performance to know which of them are using lower/higher cycles to finish the execution. HTP-specific linting profiling report provides per op cycle count on the main thread along with background execution information. On the main thread, each op has to wait for some cycles since the execution of the last op before the start of its own execution. This wait period can be attributed to various factors such as scheduling or waiting for some background HVX or DMA activity to finish. In linting profiling report, each op has a cycle count associated with it signifying the amount of cycles spent actually executing the op on the main thread. There is also a “Wait” entry associated with each op that correponds to the wait period mentioned before. Aside from these two cycle counts that describe the main thread activity, each op has two more entries to depict background activity. The first of these two entries is the “Overlap” entry denoting the number of cycles spent on at least one background op while the op is executing on the main thread. Next, each op has a “Overlap (wait)” entry that is similar to the “Wait” entry with the exception that the cycles reported in this entry correspond to the “Wait” period (ie. cycles spent on at least one background op while the main thread was waiting). Background ops that are being waited on by main thread ops are not considered as background activity and as such do not contribute to the counts reported by the overlap entries. Each of the overlap entries also has several indented lines (10 maximum) following it indicating the names of the ops that contributed to the respective overlap cycle count. Finally, each op also has a “Resources” entry listing the different resources used by that op. The HTP-specific linting profiling level can be enabled by specifying `--profiling_level=backend` when running qnn-net-run so that the profiling level specified in the backend-specific config file is used. Please refer to the documentation for qnn-net-run to learn more about libQnnHtpNetRunExtensions.so and backend-specific config files. For linting profiling, the backend-specific config file should specify the following option along with any other desired option: { .... "devices": [{ ... "profiling_level": "linting", "cores": [{ ... }] }] } Copy to clipboard The profile outputs generated with this profiling level can be viewed using the qnn-profile-viewer tool with its libQnnHtpProfilingReader.so or libQnnChrometraceProfilingReader.so reader plugin. libQnnHtpProfilingReader.so reader provides raw output of every single run whereas libQnnChrometraceProfilingReader.so provides average output of all the runs. Additionally, a file containing the profiling data in chrometrace format can be generated if an output file is specified with the `--output` option when running the qnn-profile-viewer tool with the libQnnChrometraceProfilingReader.so reader plugin. To retrieve linting information from an inference, the following steps are required: 1. Set $QNN\_SDK\_ROOT to your desired QNN version 2. Run “source $QNN\_SDK\_ROOT/bin/envsetup.sh” 3. - Push the reqired files to the device - - $QNN\_SDK\_ROOT/lib/aarch64-android/libQnnHtpNetRunExtensions.so - backend\_extension\_config.json - htp\_config.json 4. Run inference on device, make sure to add the following parameters: “-–profiling\_level=backend and –-config\_file=backend\_extension\_config.json” 5. Pull output logs to linux 6. When using qnn-profile-viewer make sure to specify the following parameter: “–-reader $QNN\_SDK\_ROOT/lib/x86\_64-linux-clang/libQnnHtpProfilingReader.so” 7. When generating chromeTrace file, make sure to specify the following parameter: “–output ./chrometrace.json” **backend\_extension\_config.json** { "backend_extensions": { "shared_library_path" : "./libQnnHtpNetRunExtensions.so", "config_file_path" : "./htp_config.json" } } Copy to clipboard **htp\_config.json** {"devices": [ {"profiling_level" : "linting"} ] } Copy to clipboard **Example Inference Command** ./qnn-net-run --retrieve_context sample_model.bin --backend libQnnHtp.so --input_list target_raw_list.txt --config_file backend_extension_config.json --output_dir output_htp --profiling_level backend Copy to clipboard **Example Profile Viewer Command** $QNN_SDK_ROOT/bin/x86_64-linux-clang/qnn-profile-viewer --reader $QNN_SDK_ROOT/lib/x86_64-linux-clang/libQnnHtpProfilingReader.so --input_log ./output/qnn-profiling-data_0.log $QNN_SDK_ROOT/bin/x86_64-linux-clang/qnn-profile-viewer --reader $QNN_SDK_ROOT/lib/x86_64-linux-clang/libQnnChrometraceProfilingReader.so --input_log ./output/qnn-profiling-data_0.log --output ./chromeTrace.json Copy to clipboard [Showcase Model 1](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-profiling-showcase-model-original1-figure) diagram illustrates a model with two branches each performing a couple of convolutions before their results are used in a sub operation. **Showcase Model 1** ![QNN HTP Profiling Showcase Model 1](data:image/png;base64,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) The linting profiling output for this model is given below: Execute Stats (Average): ------------------------ Total Inference Time: --------------------- NetRun: 16792 us Backend (RPC (execute) time): 16242 us Backend (QNN accelerator (execute) time): 15190 us Backend (Num times yield occured): 0 count Backend (Time for initial VTCM acquire): 0 us Backend (Time for HVX + HMX power on and acquire): 0 us Backend (Accelerator (critical path execute) time (cycles)): 4327266 cycles Input OpId_2 (cycles): 0 cycles Wait (Scheduler) time: 0 cycles Overlap time: 0 cycles Overlap (wait) time: 0 cycles Resources: OpId_0 (cycles): 8036 cycles Wait (Scheduler) time: 629 cycles Overlap time: 4770 cycles Overlap (wait) time: 565 cycles Resources: model_convStart_Conv2D:OpId_21 (cycles): 147075 cycles Wait (Scheduler) time: 32 cycles Overlap time: 85292 cycles model_sub_sub:OpId_57 Output OpId_3 model_add_add:OpId_58 model_tf_op_layer_stride_stride:OpId_24 model_convStart_Conv2D:OpId_21 Overlap (wait) time: 32 cycles model_convStart_Conv2D:OpId_21 Resources: HVX, HMX, DMA model_tf_op_layer_stride_stride:OpId_24 (cycles): 146494 cycles Wait (Scheduler) time: 0 cycles Overlap time: 70807 cycles model_add_add:OpId_58 Output OpId_3 model_convStart_Conv2D:OpId_21 model_tf_op_layer_stride_stride:OpId_24 Overlap (wait) time: 0 cycles Resources: HVX model_convLeft1_Conv2D:OpId_34 (cycles): 288249 cycles Wait (Scheduler) time: 425 cycles Overlap time: 195988 cycles Output OpId_3 model_add_add:OpId_58 model_tf_op_layer_stride_stride:OpId_24 model_convStart_Conv2D:OpId_21 Overlap (wait) time: 304 cycles Output OpId_3 model_add_add:OpId_58 model_convStart_Conv2D:OpId_21 Resources: HMX, DMA model_convRight1_Conv2D:OpId_41 (cycles): 220391 cycles Wait (Scheduler) time: 803 cycles Overlap time: 135268 cycles Output OpId_3 model_add_add:OpId_58 model_tf_op_layer_stride_stride:OpId_24 model_convStart_Conv2D:OpId_21 Overlap (wait) time: 557 cycles Output OpId_3 model_tf_op_layer_stride_stride:OpId_24 model_convStart_Conv2D:OpId_21 Resources: HMX, DMA model_convRight2_Conv2D:OpId_48 (cycles): 181016 cycles Wait (Scheduler) time: 1090 cycles Overlap time: 69323 cycles model_sub_sub:OpId_57 model_convStart_Conv2D:OpId_21 Output OpId_3 model_add_add:OpId_58 Overlap (wait) time: 489 cycles model_sub_sub:OpId_57 model_convStart_Conv2D:OpId_21 Output OpId_3 model_add_add:OpId_58 Resources: HMX, DMA model_convLeft2_Conv2D:OpId_55 (cycles): 233736 cycles Wait (Scheduler) time: 1059 cycles Overlap time: 93020 cycles model_sub_sub:OpId_57 model_convStart_Conv2D:OpId_21 Output OpId_3 model_add_add:OpId_58 model_tf_op_layer_stride_stride:OpId_24 Overlap (wait) time: 464 cycles model_sub_sub:OpId_57 model_convStart_Conv2D:OpId_21 Output OpId_3 model_add_add:OpId_58 Resources: HMX, DMA model_sub_sub:OpId_57 (cycles): 2165162 cycles Wait (Scheduler) time: 0 cycles Overlap time: 465046 cycles model_sub_sub:OpId_57 Output OpId_3 model_add_add:OpId_58 model_convStart_Conv2D:OpId_21 model_tf_op_layer_stride_stride:OpId_24 Overlap (wait) time: 0 cycles Resources: HVX model_add_add:OpId_58 (cycles): 525971 cycles Wait (Scheduler) time: 0 cycles Overlap time: 481468 cycles model_tf_op_layer_stride_stride:OpId_24 model_convStart_Conv2D:OpId_21 Output OpId_3 model_add_add:OpId_58 Overlap (wait) time: 0 cycles Resources: HVX Output OpId_3 (cycles): 407091 cycles Wait (Scheduler) time: 0 cycles Overlap time: 115120 cycles Overlap (wait) time: 0 cycles Resources: HVX Copy to clipboard The linting profiling chrometrace output for this model is given below: **Showcase Model 1 Chrometrace** ![QNN HTP Profiling Showcase Model 1 Chrometrace](data:image/png;base64,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) From the output, it is evident that the sub op (OpId\_57) is the most significant contributor to the total execution time - around 50%. This op also does not have significant parallel op execution - its Overlap time is 465046 cycles which is about 21.5% of its total execution time - indicating that this op is a good bottleneck to optimize. We can design an equivalent model as shown in the [Showcase Model 1 Optimized](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-profiling-showcase-model-optimized1-figure) diagram merging the two branches and replacing the sub op with a convolution with weights manually designed such that it performs the same task as a sub op. **Showcase Model 1 Optimized** ![QNN HTP Profiling Showcase Model 1 Optimized](data:image/png;base64,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) The linting profiling output for this optimized model is given below: Execute Stats (Average): ------------------------ Total Inference Time: --------------------- NetRun: 11884 us Backend (RPC (execute) time): 11525 us Backend (QNN accelerator (execute) time): 10481 us Backend (Num times yield occured): 0 count Backend (Time for initial VTCM acquire): 0 us Backend (Time for HVX + HMX power on and acquire): 0 us Backend (Accelerator (critical path execute) time (cycles)): 1374349 cycles Input OpId_2 (cycles): 0 cycles Wait (Scheduler) time: 0 cycles Overlap time: 0 cycles Overlap (wait) time: 0 cycles Resources: OpId_0 (cycles): 3500 cycles Wait (Scheduler) time: 1284 cycles Overlap time: 3221 cycles Overlap (wait) time: 1268 cycles Resources: model_convStart_Conv2D:OpId_21 (cycles): 487448 cycles Wait (Scheduler) time: 32 cycles Overlap time: 475888 cycles Output OpId_3 model_add_add:OpId_50 model_tf_op_layer_stride_1_stride_1:OpId_24 model_convStart_Conv2D:OpId_21 Overlap (wait) time: 32 cycles model_convStart_Conv2D:OpId_21 Resources: HVX, HMX, DMA model_tf_op_layer_stride_1_stride_1:OpId_24 (cycles): 10422 cycles Wait (Scheduler) time: 0 cycles Overlap time: 10075 cycles model_convStart_Conv2D:OpId_21 model_tf_op_layer_stride_1_stride_1:OpId_24 Overlap (wait) time: 0 cycles Resources: HVX model_convCombined1_Conv2D:OpId_34 (cycles): 337711 cycles Wait (Scheduler) time: 82 cycles Overlap time: 307394 cycles Output OpId_3 model_tf_op_layer_stride_1_stride_1:OpId_24 model_convStart_Conv2D:OpId_21 Overlap (wait) time: 50 cycles Output OpId_3 model_convStart_Conv2D:OpId_21 Resources: HMX, DMA model_convCombined2_Conv2D:OpId_41 (cycles): 295022 cycles Wait (Scheduler) time: 1184 cycles Overlap time: 286062 cycles model_add_add:OpId_50 Output OpId_3 model_convStart_Conv2D:OpId_21 model_tf_op_layer_stride_1_stride_1:OpId_24 Overlap (wait) time: 1140 cycles model_add_add:OpId_50 Output OpId_3 model_convStart_Conv2D:OpId_21 model_tf_op_layer_stride_1_stride_1:OpId_24 Resources: HMX, DMA model_subConv_Conv2D:OpId_48 (cycles): 48720 cycles Wait (Scheduler) time: 1186 cycles Overlap time: 46686 cycles model_add_add:OpId_50 model_tf_op_layer_stride_1_stride_1:OpId_24 Output OpId_3 model_convStart_Conv2D:OpId_21 Overlap (wait) time: 1142 cycles model_add_add:OpId_50 Output OpId_3 model_convStart_Conv2D:OpId_21 Resources: HMX, DMA model_add_add:OpId_50 (cycles): 110698 cycles Wait (Scheduler) time: 0 cycles Overlap time: 108524 cycles model_add_add:OpId_50 Output OpId_3 model_convStart_Conv2D:OpId_21 model_tf_op_layer_stride_1_stride_1:OpId_24 Overlap (wait) time: 0 cycles Resources: HVX Output OpId_3 (cycles): 77054 cycles Wait (Scheduler) time: 0 cycles Overlap time: 75438 cycles Overlap (wait) time: 0 cycles Resources: HVX Copy to clipboard The total execution time has decreased significantly as a result of removing the sub op. All the ops also have significant amount of parallel op execution - as evidenced by their respective Overlap time numbers - indicating good optimization. [Showcase Model 2](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-profiling-showcase-model-original2-figure) diagram illustrates a model that is similar to the one in the [Showcase Model 1](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-profiling-showcase-model-original1-figure) diagram. The difference is that there is a div op in place of the problematic sub op. **Showcase Model 2** ![QNN HTP Profiling Showcase Model 2](data:image/png;base64,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) The linting profiling output for this model is given below: Execute Stats (Average): ------------------------ Total Inference Time: --------------------- NetRun: 19353 us Backend (RPC (execute) time): 18679 us Backend (QNN accelerator (execute) time): 17700 us Backend (Num times yield occured): 0 count Backend (Time for initial VTCM acquire): 0 us Backend (Time for HVX + HMX power on and acquire): 0 us Backend (Accelerator (critical path execute) time (cycles)): 7866535 cycles Input OpId_2 (cycles): 0 cycles Wait (Scheduler) time: 0 cycles Overlap time: 0 cycles Overlap (wait) time: 0 cycles Resources: OpId_0 (cycles): 8657 cycles Wait (Scheduler) time: 782 cycles Overlap time: 5155 cycles Overlap (wait) time: 717 cycles Resources: model_convStart_Conv2D:OpId_21 (cycles): 148293 cycles Wait (Scheduler) time: 34 cycles Overlap time: 86500 cycles model_tf_op_layer_RealDiv_RealDiv:OpId_57 Output OpId_3 model_add_add:OpId_58 model_tf_op_layer_stride_stride:OpId_24 model_convStart_Conv2D:OpId_21 Overlap (wait) time: 34 cycles model_convStart_Conv2D:OpId_21 Resources: HVX, HMX, DMA model_tf_op_layer_stride_stride:OpId_24 (cycles): 145084 cycles Wait (Scheduler) time: 0 cycles Overlap time: 70877 cycles model_convStart_Conv2D:OpId_21 model_add_add:OpId_58 Output OpId_3 model_tf_op_layer_stride_stride:OpId_24 Overlap (wait) time: 0 cycles Resources: HVX model_convLeft1_Conv2D:OpId_34 (cycles): 285476 cycles Wait (Scheduler) time: 431 cycles Overlap time: 196212 cycles Output OpId_3 model_tf_op_layer_stride_stride:OpId_24 model_convStart_Conv2D:OpId_21 Overlap (wait) time: 318 cycles Output OpId_3 model_tf_op_layer_stride_stride:OpId_24 model_convStart_Conv2D:OpId_21 Resources: HMX, DMA model_convRight1_Conv2D:OpId_41 (cycles): 219298 cycles Wait (Scheduler) time: 804 cycles Overlap time: 134711 cycles Output OpId_3 model_tf_op_layer_stride_stride:OpId_24 model_convStart_Conv2D:OpId_21 Overlap (wait) time: 558 cycles Output OpId_3 model_tf_op_layer_stride_stride:OpId_24 model_convStart_Conv2D:OpId_21 Resources: HMX, DMA model_convRight2_Conv2D:OpId_48 (cycles): 181198 cycles Wait (Scheduler) time: 1083 cycles Overlap time: 68306 cycles model_tf_op_layer_RealDiv_RealDiv:OpId_57 Output OpId_3 model_tf_op_layer_stride_stride:OpId_24 Overlap (wait) time: 476 cycles model_tf_op_layer_RealDiv_RealDiv:OpId_57 Output OpId_3 Resources: HMX, DMA model_convLeft2_Conv2D:OpId_55 (cycles): 233731 cycles Wait (Scheduler) time: 1055 cycles Overlap time: 91960 cycles model_tf_op_layer_RealDiv_RealDiv:OpId_57 Output OpId_3 model_add_add:OpId_58 model_tf_op_layer_stride_stride:OpId_24 Overlap (wait) time: 447 cycles model_tf_op_layer_RealDiv_RealDiv:OpId_57 Output OpId_3 Resources: HMX, DMA model_tf_op_layer_RealDiv_RealDiv:OpId_57 (cycles): 5344081 cycles Wait (Scheduler) time: 0 cycles Overlap time: 528123 cycles model_tf_op_layer_RealDiv_RealDiv:OpId_57 Output OpId_3 model_add_add:OpId_58 model_convStart_Conv2D:OpId_21 model_tf_op_layer_stride_stride:OpId_24 Overlap (wait) time: 0 cycles Resources: HVX model_add_add:OpId_58 (cycles): 525199 cycles Wait (Scheduler) time: 0 cycles Overlap time: 481084 cycles model_convStart_Conv2D:OpId_21 model_tf_op_layer_stride_stride:OpId_24 Output OpId_3 model_add_add:OpId_58 Overlap (wait) time: 0 cycles Resources: HVX Output OpId_3 (cycles): 771320 cycles Wait (Scheduler) time: 0 cycles Overlap time: 115729 cycles Overlap (wait) time: 0 cycles Resources: HVX Copy to clipboard Again, the bottleneck for this graph can be identified by examining the main and background utilization of each op. In this case, the div op is the major contributor to the overall graph execution time with it taking up 5344081 cycles - about 68% of the total execution time. Only about 10% of this op’s execution has some parallel background activity which again indicates a good potential for performance gain through optimization. Replacing the div op with a mul op is a suggested optimization strategy found in the best practices guidelines. The linting profiler output for the graph optimized with a mult op instead of a div op is given below: Execute Stats (Average): ------------------------ Total Inference Time: --------------------- NetRun: 15755 us Backend (RPC (execute) time): 15274 us Backend (QNN accelerator (execute) time): 14108 us Backend (Num times yield occured): 0 count Backend (Time for initial VTCM acquire): 0 us Backend (Time for HVX + HMX power on and acquire): 0 us Backend (Accelerator (critical path execute) time (cycles)): 2741387 cycles Input OpId_2 (cycles): 0 cycles Wait (Scheduler) time: 0 cycles Overlap time: 0 cycles Overlap (wait) time: 0 cycles Resources: OpId_0 (cycles): 8067 cycles Wait (Scheduler) time: 735 cycles Overlap time: 4781 cycles Overlap (wait) time: 669 cycles Resources: model_convStart_Conv2D:OpId_21 (cycles): 147478 cycles Wait (Scheduler) time: 32 cycles Overlap time: 86319 cycles model_multiply_mul:OpId_57 Output OpId_3 model_add_add:OpId_58 model_tf_op_layer_stride_stride:OpId_24 model_convStart_Conv2D:OpId_21 Overlap (wait) time: 32 cycles model_convStart_Conv2D:OpId_21 Resources: HVX, HMX, DMA model_tf_op_layer_stride_stride:OpId_24 (cycles): 145396 cycles Wait (Scheduler) time: 0 cycles Overlap time: 70208 cycles model_convStart_Conv2D:OpId_21 model_add_add:OpId_58 Output OpId_3 model_tf_op_layer_stride_stride:OpId_24 Overlap (wait) time: 0 cycles Resources: HVX model_convLeft1_Conv2D:OpId_34 (cycles): 287130 cycles Wait (Scheduler) time: 430 cycles Overlap time: 198222 cycles Output OpId_3 model_add_add:OpId_58 model_tf_op_layer_stride_stride:OpId_24 model_convStart_Conv2D:OpId_21 Overlap (wait) time: 308 cycles Output OpId_3 model_add_add:OpId_58 model_tf_op_layer_stride_stride:OpId_24 model_convStart_Conv2D:OpId_21 Resources: HMX, DMA model_convRight1_Conv2D:OpId_41 (cycles): 219409 cycles Wait (Scheduler) time: 806 cycles Overlap time: 135286 cycles Output OpId_3 model_add_add:OpId_58 model_tf_op_layer_stride_stride:OpId_24 model_convStart_Conv2D:OpId_21 Overlap (wait) time: 558 cycles Output OpId_3 model_tf_op_layer_stride_stride:OpId_24 model_convStart_Conv2D:OpId_21 Resources: HMX, DMA model_convRight2_Conv2D:OpId_48 (cycles): 181465 cycles Wait (Scheduler) time: 1068 cycles Overlap time: 69160 cycles model_multiply_mul:OpId_57 model_convStart_Conv2D:OpId_21 Output OpId_3 model_add_add:OpId_58 model_tf_op_layer_stride_stride:OpId_24 Overlap (wait) time: 467 cycles model_multiply_mul:OpId_57 model_convStart_Conv2D:OpId_21 Output OpId_3 model_add_add:OpId_58 Resources: HMX, DMA model_convLeft2_Conv2D:OpId_55 (cycles): 233619 cycles Wait (Scheduler) time: 1055 cycles Overlap time: 92740 cycles model_multiply_mul:OpId_57 model_convStart_Conv2D:OpId_21 Output OpId_3 model_add_add:OpId_58 model_tf_op_layer_stride_stride:OpId_24 Overlap (wait) time: 445 cycles model_multiply_mul:OpId_57 model_convStart_Conv2D:OpId_21 Output OpId_3 model_add_add:OpId_58 Resources: HMX, DMA model_multiply_mul:OpId_57 (cycles): 737978 cycles Wait (Scheduler) time: 0 cycles Overlap time: 437784 cycles model_multiply_mul:OpId_57 Output OpId_3 model_add_add:OpId_58 model_convStart_Conv2D:OpId_21 model_tf_op_layer_stride_stride:OpId_24 Overlap (wait) time: 0 cycles Resources: HVX model_add_add:OpId_58 (cycles): 527450 cycles Wait (Scheduler) time: 0 cycles Overlap time: 481714 cycles model_convStart_Conv2D:OpId_21 model_tf_op_layer_stride_stride:OpId_24 Output OpId_3 model_add_add:OpId_58 Overlap (wait) time: 0 cycles Resources: HVX Output OpId_3 (cycles): 249264 cycles Wait (Scheduler) time: 0 cycles Overlap time: 117890 cycles Overlap (wait) time: 0 cycles Resources: HVX Copy to clipboard There is a noticeable reduction in the total graph execute time and the ops also have better background utilization indicating better optimization than before. Next, [Showcase Model 3](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-profiling-showcase-model-original3-figure) diagram illustrates a model that is similar to the one in [Showcase Model 1 Optimized](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-profiling-showcase-model-optimized1-figure) diagram. The difference is that the ReLU ops have been replaced with PReLU ops. **Showcase Model 3** ![QNN HTP Profiling Showcase Model 3](data:image/png;base64,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) The linting profiler output for this model is given below: Execute Stats (Average): ------------------------ Total Inference Time: --------------------- NetRun: 15368 us Backend (RPC (execute) time): 15033 us Backend (QNN accelerator (execute) time): 13863 us Backend (Num times yield occured): 0 count Backend (Time for initial VTCM acquire): 0 us Backend (Time for HVX + HMX power on and acquire): 0 us Backend (Accelerator (critical path execute) time (cycles)): 2789467 cycles Input OpId_2 (cycles): 0 cycles Wait (Scheduler) time: 0 cycles Overlap time: 0 cycles Overlap (wait) time: 0 cycles Resources: OpId_0 (cycles): 3411 cycles Wait (Scheduler) time: 1226 cycles Overlap time: 3173 cycles Overlap (wait) time: 1194 cycles Resources: model_convStart_Conv2D:OpId_21 (cycles): 589431 cycles Wait (Scheduler) time: 957 cycles Overlap time: 41199 cycles Output OpId_3 model_add_add:OpId_54 model_preluCombined1_add:OpId_37 model_convStart_Conv2D:OpId_21 Overlap (wait) time: 72 cycles Output OpId_3 model_convStart_Conv2D:OpId_21 Resources: HVX, HMX, DMA model_tf_op_layer_stride_1_stride_1:OpId_24 (cycles): 0 cycles Wait (Scheduler) time: 0 cycles Overlap time: 0 cycles Overlap (wait) time: 0 cycles Resources: model_convCombined1_Conv2D:OpId_34 (cycles): 165119 cycles Wait (Scheduler) time: 1089 cycles Overlap time: 155164 cycles model_preluCombined1_add:OpId_37 Output OpId_3 model_add_add:OpId_54 model_convStart_Conv2D:OpId_21 Overlap (wait) time: 977 cycles model_preluCombined1_add:OpId_37 Output OpId_3 model_add_add:OpId_54 model_convStart_Conv2D:OpId_21 Resources: HMX, DMA model_preluCombined1_add:OpId_37 (cycles): 27315 cycles Wait (Scheduler) time: 0 cycles Overlap time: 9431 cycles model_convStart_Conv2D:OpId_21 Overlap (wait) time: 0 cycles Resources: HVX model_convCombined2_Conv2D:OpId_43 (cycles): 805490 cycles Wait (Scheduler) time: 81 cycles Overlap time: 251743 cycles model_add_add:OpId_54 Output OpId_3 model_preluCombined1_add:OpId_37 model_preluCombined2_add:OpId_46 model_convStart_Conv2D:OpId_21 Overlap (wait) time: 62 cycles Output OpId_3 model_convStart_Conv2D:OpId_21 Resources: HMX, DMA model_preluCombined2_add:OpId_46 (cycles): 0 cycles Wait (Scheduler) time: 0 cycles Overlap time: 0 cycles Overlap (wait) time: 0 cycles Resources: HVX model_subConv_Conv2D:OpId_52 (cycles): 666721 cycles Wait (Scheduler) time: 34 cycles Overlap time: 180805 cycles model_add_add:OpId_54 Output OpId_3 model_convStart_Conv2D:OpId_21 model_preluCombined2_add:OpId_46 Overlap (wait) time: 13 cycles model_convStart_Conv2D:OpId_21 Resources: HMX, DMA model_add_add:OpId_54 (cycles): 62806 cycles Wait (Scheduler) time: 0 cycles Overlap time: 57481 cycles model_add_add:OpId_54 Output OpId_3 model_preluCombined1_add:OpId_37 model_preluCombined2_add:OpId_46 model_convStart_Conv2D:OpId_21 Overlap (wait) time: 0 cycles Resources: HVX Output OpId_3 (cycles): 465781 cycles Wait (Scheduler) time: 0 cycles Overlap time: 430560 cycles Overlap (wait) time: 0 cycles Resources: HVX Copy to clipboard The usual sign indicating bottlenecks is present here as well. There are multiple ops with low parallel execution. PReLU ops are some of the background ops that executed for these ops and the best practices guidelines suggest that PReLU ops should be replaced with ReLU ops. Changing the graph by replacing the PReLU ops with ReLU gives us the same model as the one shown in the [Showcase Model 1 Optimized](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-profiling-showcase-model-optimized1-figure) diagram which is much better optimized as explained before. ## QNN HTP Optrace Profiling **Optrace High-level Operation** [HTP Optrace Block Diagram](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-optrace-block-figure) illustrates the high-level operation of Optrace profiling. **HTP Optrace Block Diagram** ![HTP Optrace Block Diagram](data:image/png;base64,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) [HTP Optrace Tooling Call Flow Diagram](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-optrace-call-flow-figure) illustrates the basic Optrace profiling events and how they are captured. **HTP Optrace Tooling Call Flow Diagram** ![HTP Optrace Tooling Call Flow Diagram](data:image/png;base64,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) **Detailed Command Line Usage Guide** To enable and use Optrace profiling, we need to specify additional parameters during model preparation, model execution, and post-processing. The following sections give a detailed usage guide for the command line parameters needed, with examples. **Converter** For converter, you can optionally set these two parameters: - `--export_format dlc` - `--enable_framework_trace` This is an option specific to generate a dlc file, which allows the chrometrace to be aware of framework name, as well as the QNN Op types that those get converted into. It is recommended that you enable these options for additional context whenever possible. **Preparation (Context Binary Generation)** For context binary generation, we require two additional parameters: - `--profiling_level detailed` - `--profiling_option optrace` By using these parameters, the result is an extra file outputted in the current working directory of qnn-context-binary-generator: the schematic binary Sample Command Line Below: qnn-context-binary-generator --profiling_level detailed --profiling_option optrace --backend [SDK_PATH]/lib/x86_64-linux-clang/libQnnHtp.so --model [MODEL].so --config_file HtpConfigFile.json --output_dir [OUTPUT_DIR] --binary_file [CONTEXT_BIN] Copy to clipboard From the above command here are the following outputs: - `[CONTEXT_BIN]` - this is the context binary file containing the model - `[MODEL]_schematic.bin` - this is the schematic file (required for chrometrace generation) **Execution (Net Run)** For qnn-net-run, we require two additional parameters: - `--profiling_level detailed` - `--profiling_option optrace` This will allow for profiling events to be embedded into the output profiling log. Sample Command Line Below: qnn-net-run --profiling_level detailed --profiling_option optrace --output_data_type float_and_native --retrieve_context [CONTEXT_BIN] --backend libQnnHtp.so --input_list ./inputs/input_list.txt --output_dir . --log_level info Copy to clipboard From the above command here are the following outputs: - `qnn-profiling-data.log` - this is the log data containing the optrace information that will be further parsed in the post process step (required for chrometrace generation) - The normal outputs of model execution **Post Process (Chrometrace Generation)** During the post process phase, we now have the required data from the above two steps to generate a chrometrace. - `[MODEL]_schematic.bin` - this is the schematic we recieve from the context binary generation step - `qnn-profiling-data.log` - this is the detailed profiling data we recieve from the execution step To generate the chrometrace, we run qnn-profile-viewer on the host with the libQnnHtpOptraceProfilingReader reader library. Sample Command Line Below: qnn-profile-viewer --config [PATH_TO]/config.json --reader [SDK_PATH]/lib/[TARGET]/libQnnHtpOptraceProfilingReader.so --input_log ./qnn-profiling-data.log --schematic ./[MODEL]_schematic.bin --output ./chrometrace.json Copy to clipboard The config.json file provides parameters beyond the ones covered in the command line arguments, such as the following: - `enable_input_output_flow_events` - Adds flow events to the chrometrace.json, showing input-output dependencies between operations. Requires using the legacy UI to open chrometrace. - `enable_sequencer_flow_events` - Adds flow events to the chrometrace.json, showing ordering dependencies between operations, imposed by the sequencer. Requires using the legacy UI to open chrometrace. - `htp_json` - Dumps a [NAME]\_htp.json file containing the toplogy and op-by-op information about the HTP graph. Default is on. - `runtrace` - Adds Runtrace execution and preemption events (if available) at the bottom of each core in the output chrometrace. Default is on. - `memory_info` - Adds memory bandwidth and allocation graphs (if available) at the bottom of each core in the output chrometrace. Default is on. - `traceback` - Adds trace back to source framework in the output chrometrace. Default is on. - `qhas_schema` - Dumps a qhas\_schema.json that can be used to validate the QHAS json file. Default is off. - `qhas_json` - Dumps a [model]\_qnn\_htp\_analysis\_summary.json. Default is off. Sample config.json below, with all available boolean parameters: { "features": { "enable_input_output_flow_events": true, "enable_sequencer_flow_events": true, "htp_json": true, "runtrace": true, "memory_info": true, "traceback": true, "qhas_schema": true, "qhas_json": true } } Copy to clipboard From the above command here are the following outputs: - `chrometrace.json` - the chrometrace output that can be opened with either the [Perfetto Trace Visualizer](https://ui.perfetto.dev) or with `chrome://tracing` - `chrometrace_qnn_htp_analysis_summary.html` - the QHAS HTML report Note A number of ops in HTP get classified as “System Service”. These are not ops associated with any specific operation performed in the base neural network. Each System Service category is briefly explained below: - DramToTcm: Loads data from DRAM into VTCM. - TcmToDram: Writes data from VTCM into DRAM. - Sync Op: Used to provide an ordering for HVX ops to resolve dependencies between ops. - DmaCheckpointSet: A producer writing to memory for a future consumer to use sets this checkpoint when it is finished writing. - DmaCheckpointWait: A consumer that waits for DmaCheckpointSet. - BlockZapOp: When a tensor’s data is not a multiple of block-size, this operation “pads” the blocks with a specified zero-value. - SystemService: Not associated with any specific parent op, it prefetches chunks of data into L2 cache for ops to use. If you want additional context such as framework names and QNN Op types, provide the DLC file with the following parameter: - `--dlc ./[MODEL].dlc` This DLC file is generated within the converter as mentioned above. Optionally, you can perform a profile submodule chrometrace, where you specify two QNN node names, and the generated chrometrace only represents the subnetwork contained between the two. To use this, you use the following two parameters: - `--zoom_start` - this is the starting QNN node name for the submodule. - `--zoom_end` - this is the ending QNN node name for the submodule. The parameters `--zoom_start` and `--zoom_end` can accept framework node names (e.g. ONNX op names) if the –dlc parameter is set earlier. If the –dlc parameter is set, the program will automatically detect whether the names provided match a framework name or QNN name, no further context is required. **HTP Graph Topology and per-Op Information in Netron** By enabling the `htp_json` parameter in the config.json above, a [NAME]\_htp.json file containing the toplogy and op-by-op information about the HTP graph will be dumped. This json file can be viewed directly in Netron. This feature can be used in conjunction with the node zooming feature above. [HTP Graph Topology in Netron](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#optrace-netron) demonstrates viewing [NAME]\_htp.json in Netron. **HTP Graph Topology in Netron** ![HTP Graph Topology in Netron](data:image/jpeg;base64,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) [HTP Graph per-Op Information in Netron](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#optrace-netron-node-properties) demonstrates the ability to click on an HTP node and view Op properties. **HTP Graph per-Op Information in Netron** ![HTP Graph per-Op Information in 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) **Memory Bandwidth and Allocation graphs** By enabling the `memory_info` parameter in the config.json above, memory bandwidth and allocation graphs (if available) will be displayed at the bottom of each core in the output chrometrace. The graphs below titled “VTCM”/”DRAM” “read”/”write” display instantaneous bandwidth, while the “VTCM alloc” graphs display current memory allocation at that point. [HTP Optrace Multicore Memory Graphs](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#multicore-chrometrace-memory-graphs) demonstrates the per-core memory bandwidth and allocation graphs. **HTP Optrace Multicore Memory Graphs** ![HTP Optrace Multicore Memory Graphs](data:image/png;base64,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) **Gzip Compression of Chrometrace** This feature will add the ability to automatically compress output chrometraces into the gzip format and will save massive amounts of disk space. On a sample model, the size of the chrometrace output was ~1300KB before compression and ~60KB after compression (ratio of 0.05). There are 3 ways to enable gzip compression: - Append `.gz` to the output filename command line argument for qnn-profile-viewer - Add the `--gzip` command line flag for qnn-profile-viewer - Enable the `gzip` parameter in the optrace config.json file Gzip compression will be turned on if any one of these options are set. Note Command line parameters will be subject to change once multi-graph support is enabled. **Runtrace Execution and Preemption Events** By enabling the `runtrace` parameter in the config.json above, Runtrace execution and preemption events (if available) will be displayed at the bottom of each core in the output chrometrace. The graphs below titled “<GRAPH\_NAME> Runtraces” show the duration of the entire inference as well as other information such as the time it took to acquire physical resources (execution events). The graphs below titled “<GRAPH\_NAME> Yields” show the time it took to save, re-acquire, and restore VTCM memory when yielding to a higher priority thread (preemption events). [HTP Optrace Runtrace Graph](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#runtrace-graph) demonstrates the Runtrace graph execution events for one graph. **HTP Optrace Runtrace Graph** ![HTP Optrace Runtrace Graph](data:image/png;base64,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) ## QNN HTP Analysis Summary (QHAS) From the steps above for running optrace profiling, we can see that a QHAS HTML Report is generated by `qnn-profile-viewer` as part of the existing flow (no extra parameters required). Unlike the chrometrace which visually depicts the data from the HTP ops, the QHAS HTML Report summarizes this data into a report with analysis. [QHAS HTML Report Example](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qhas-html-example-figure) illustrates the layout of the QHAS Report with the “HTP Overall Summary” section expanded. The other sections will expand once clicked. **QHAS HTML Report Example** ![QHAS HTML Report Example](data:image/png;base64,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) Additionally, a column within a section can be sorted by clicking on the sort icon. And a pie chart can be generated for a column to illustrate the value of each row relative to the total in that column, by clicking on the chart icon. In the [QHAS HTML Report Sorting and Plotting Example](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qhas-sorting-and-chart-figure), we are sorting the “Cycles” column under the “QNN Op Types” section in descending order, and are displaying a pie chart for it. This pie chart visually represents the fraction of the total cycle count that each op type uses. **QHAS HTML Report Sorting and Plotting Example** ![QHAS HTML Report Sorting and Plotting Example](data:image/png;base64,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) In the [QHAS HTML Report Filtering Example](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qhas-filtering-figure), we are filtering the columns under “HTP Op” and “QNN Op” by the keywords “conv” and “batch”, respectively. Only rows that contain the filter keyword will be shown. Filter keywords in multiple columns can be set simultaneously. **QHAS HTML Report Filtering Example** ![QHAS HTML Report Filtering Example](data:image/png;base64,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) Note The “Dominant Path” section of QHAS shows a timeline of the highest priority HTP op throughout the timeline. The priority list is as follows: - HMX Op - HVX Op - Ops performing DMA Reads - Ops performing DMA Writes - DmaCheckpointSet and DmaCheckpointWait (as explained in System Services above) - SyncOp (as explained in System Services above) Note If you enable the profile submodule feature above, QHAS will also only show an analysis for the nodes contained within the subnetwork. Note The QHAS feature is still in Beta so it is subject to change in future SDK versions. ## QNN Context Binary size The QNN Context Binary is used by QNN for execution of the neural network. Post preparation of graph, the ‘QNN Context Binary’ contains the information & optimizations for faster inference of the model. The ‘QNN Context Binary’ has larger size compared to the size of QNN model. This enlarged size results from the following reasons: - **Number of Operations**: HTP tries to run as many operations as possible in parallel. To be able to fit into the VTCM, heavy operations are split into smaller operations. This often results in increase in the number of operations which are needed to be present in Context Binary, resulting in increase in its size. For example, if each op takes 40 bytes of Context Binary and if the number of operations before and after the above optimization are 30 and 300,000, then we need 1.2 KB and 12 MB respectively in the Context Binary. The figure below shows a large Conv operation which needs to be broken up into smaller operations. ![../../_static/resources/multi_thread_and_multi_batch/large_conv_op.jpg](data:image/jpeg;base64,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- **Sequencing and Data Paging**: As the number of operations increase, the Context Binary must also need to store information about the sequence of operations and the information about data paging (which operation needs to be written to DDR and which needs to be brought back into VTCM during execution). This information also contributes to the size of prepared Context Binary. - **Constant Data in Graph**: The QNN Context Binary contains all constant data of the graph. The constant data consists of, among other things, convolution filters. These filters could be padded in the QNN context binary to represent internal HTP format for performance efficiency, causing additional size increase for the QNN context binary. ## Op Writing Guidelines This section serves as a guide for user to develop custom operations for graphs that can be run on the HTP backend. It also includes examples which will help user to become familiar with the different aspects of the HTP core software. - [QNN HTP Op Package - Common Default Package Ops Usage Examples](https://docs.qualcomm.com/doc/80-63442-50/topic/common_default_package_ops_usage_examples.html) - [QNN HTP Optimization Utility Functions Usage Examples](https://docs.qualcomm.com/doc/80-63442-50/topic/common_optimization_utility_funcs_usage_examples.html) - [HTP Core Headers for Op Packages](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_core_headers.html) - [Implementing Ops](https://docs.qualcomm.com/doc/80-63442-50/topic/implementing_ops.html) - [QNN HTP Op Package API Revision History](https://docs.qualcomm.com/doc/80-63442-50/topic/opPackage_API_version_guide.html) - [Optimization Grammar](https://docs.qualcomm.com/doc/80-63442-50/topic/optimization_grammar.html) - [QNN HTP Op Package - Relu Op Example](https://docs.qualcomm.com/doc/80-63442-50/topic/relu_example.html) - [QNN HTP-FP16 Op Package - Relu Op Example](https://docs.qualcomm.com/doc/80-63442-50/topic/relu_fp16_example.html) - [Scheduling and Allocation](https://docs.qualcomm.com/doc/80-63442-50/topic/scheduling_and_allocation.html) - [Allocate Memory for Scratch Buffers](https://docs.qualcomm.com/doc/80-63442-50/topic/scratch_buffer.html) - [Tensors and Memory Layout](https://docs.qualcomm.com/doc/80-63442-50/topic/tensors_and_memory_layout.html) - [Writing QNN HTP Op Package](https://docs.qualcomm.com/doc/80-63442-50/topic/writing_op_package.html) - [General OpPackage Central Migration Guidance](https://docs.qualcomm.com/doc/80-63442-50/topic/central_migration_guidance.html) - [Op Package Migration Guide](https://docs.qualcomm.com/doc/80-63442-50/topic/writing_opPackage_migration_guide.html) ## Recommendations for Network Design The HTP supports A8, A16 and FP16 activations. Generally, the accuracy and the power and energy requirements follow the order A8 < A16 < FP16. Therefore, to minimize power, one should first try the A8 mode and check for accuracy of the results. If the accuracy is not sufficient try A16 mode and if even that doesn’t achieve the desired accuracy move to FP16 mode. The following sections cover some of the best practices in graph design that allows for the optimal use of HTP hardware from a performance and accuracy perspective. - [Avoid Low Depth Activations](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_guidelines_low_depth_activations.html) - [Avoid Low Depth Activations (more examples)](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_guidelines_low_depth_activation_more_examples.html) - [Use Space-to-depth Transformation where possible](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_guidelines_space_to_depth.html) - [Reducing TCM Requirements for Performance and Functionality](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_guidelines_tcm_requirements.html) - [Choice of Activation Functions](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_guidelines_activation_functions.html) - [Number of Channels](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_guidelines_channels.html) - [Quantized 16 bit activations (A16) vs FP16 and Activation Fusion: Performance and power differences](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_guidelines_a16_vs_fp16.html) - [INT4 encodings for weights](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_guidelines_int4_weights.html) - [Other Performance and Energy Guidelines](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_guidelines_other_perf.html) It is recommended to always use symmetrical quantization of weights when quantizing the model to obtain best accuracy on HTP based targets. Activation data is recommended to be asymmetric. It is recommended to use quantization aware training as much as possible to improve the accuracy of models especially in the case of high resolution image transformation models. Using quantization aware training may take away the need to use 16bit activations and may allow the use of 8bit activations which will improve both performance and power. When using quantization aware training, keep in mind the following: > > > 1. A comparison of outputs in original framework between original float model and model with fakequant nodes helps in determining the quality of the quantization aware trained model > 2. Ensure there are fakequant nodes for all layers/kernels A16W16 (int16 weight along with uint16 activations) is supported for several Convolution type of operations. This is commonly used on image enhancement networks, but available to other type of usecases as well. This feature is enabled only on selected SoCs. For further accuracy enhancement purpose, per-channel quantization method will be added in the future. - Expectations of comparison between A16W16 models and A16W8 and FP16 models as follows: - 1. A16W16 models are expected to achieve better accuracy than A16W8 models with Post-Training quantization. 2. A16W16 models are expected to achieve better power efficiency than FP16 models while maintaining a similar accuracy result. - List of Convolution type of operations supported for A16W16: - 1. Conv2d 2. DepthConv2d 3. TransposeConv2D 4. FullyConnected 5. Matmul 6. Batchnorm 7. LayerNorm All weights/filters need to be symmetrically quantized. For Matmul, Input A must be asymmetrically quantized, Input B must be symmetrically quantized. Please refer to [HTP Backend Op Definition Supplement](https://docs.qualcomm.com/doc/80-63442-50/topic/HtpOpDefSupplement.html#htp-backend-op-definition-supplement) for details. *Limitation*: Due to Hexagon hardware limitation, INT16 weight have a special limitation that the range of weight value will be 0x8000 to 0x7F7F instead of a full 16bit range 0x8000 to 0x7FFF. Please add `--restrict_quantization_steps "-0x8000 0x7F7F"` to quantizer options when using A16W16. ## Yielding and Pre-Emption Yielding and Pre-Emption is a cooperative user-based implementation of context switching. The following document aims to help the user understand different concurrency scenarios and their expected behaviour. - [HTP Yielding](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_yielding.html) ## Byte-Granularity VTCM Starting with hexagon-v81, QNN HTP now supports byte-granularity VTCM size requests, allowing the amount of VTCM to be reserved and utilized by a graph to be specified in bytes (previously only MB requests were supported). This flexibility enables more efficient use of [VTCM Sharing](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_vtcm_sharing.html#htp-vtcm-sharing), being able to specify overmaps precise to the requirements of the secondary entity, thereby maximizing the VTCM available to QNN graphs. The byte-granularity VTCM size request can be configured in the [QNN HTP Backend Extensions](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#qnn-htp-backend-extensions) via the new parameter `"vtcm_size"`, similar to the existing `"vtcm_mb"`. The following example configuration illustrates the use of the new parameter: { "graphs": [ { "vtcm_size": 8323072, "graph_names": [...], ... } ], "devices": [{ ... }] } Copy to clipboard Note The new byte-granularity and existing MB-granularity APIs are both supported simultaneously, with neither taking precedence. Multiple VTCM size requests with the different APIs behaves similarly to multiple requests with the existing API. Warning While the API supports byte-granularity, the minimum granularity may have higher hardware-specific constraints. Presently, requests need to be a multiple of `64KB`, although this requirement may be relaxed in the future. ## VTCM Sharing Staring from hexagon-v73, it is possible for other threads in the same process to share VTCM resources with QNN HTP using the procedure described in the following pages: - [HTP VTCM Sharing](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_vtcm_sharing.html) - [VTCM Windowing](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_vtcm_sharing.html#vtcm-windowing) ## SubSystem Restart (SSR) A QNN HTP BE specific feature that allows the CDSP subsystem to automatically restart an invalidated connection after crashing. Details are provided in the following page: - [QNN HTP SSR](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_ssr.html) ## Qmem Graph (shared\_buffer only graph) A QNN HTP BE specific feature that allows users to use data buffers for shared access in between processing domains in QNN HTP backend. Using shared buffers can eliminate data copy in between client code on the host CPU and HTP accelerator. - [QNN HTP Qmem Graph](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_qmem_graph.html) ## HTP Session & Artifact Usage Guidlines ### Supported Library Use QNN only supports one set of libraries per-process. These libraries must be of the same SDK version and have matching Hexagon architecture versions. For illustrative purposes, V73 Hexagon architecture libraries will be used in the diagrams; the same guidelines apply for non-V73 artifacts as well. The supported library layout for QNN is displayed below. ![../../_static/resources/qnn_htp_supported_library_usage.png](data:image/png;base64,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) To prevent incorrect QNN library layouts, Qualcomm recommends the following: - One copy of each library should be present for a single process (backend, stub, skel, etc). - The backend library (libQnnHtp.so) should be explicitly loaded with dlopen rather than being dynamically linked as a dependency. - During the loading of Stub.so and Prepare.so, QNN first searches for them in the path of libQnnHtp.so. If not found, it searches in LD\_LIBRARY\_PATH. - Libraries should be in the same directory as one another (the skel is an exception to this so long as the ADSP\_LIBRARY\_PATH is correctly set to find the library). - Do not rename libraries to load multiple copies as this is not supported. ### Unsupported Library Use QNN does not support multiple copies of the QNN backend library (libQnnHtp.so) being accessed in a single process. Two different layouts are depicted below where multiple backend libraries are present on device. ![../../_static/resources/qnn_htp_unsupported_library_usage.png](data:image/png;base64,UklGRhpZAABXRUJQVlA4IA5ZAAAwrAGdASreA50BPwF6tVQrJ6sjJJTrYWAgCU3fhZEfwh/XuGeKvfA9wPVcMW6W74ON+Zn97/b/EBkXnK7syzX1RcvPu7K3937/vo9/s3qDfpn07fMn+2/7ge8b6Uf8v6hP9V9JD/2ezR/e/UA86r1hf8z/4f3K97P0AP//7dvSj9Rv8F/dfXb4afpf7344/kf1L+1/vH7u+unkP9I/oP/J/svVP+Yffv+b/ffO//m/378mvR35F/THsEe0v+VvQOv/7b/vf572CPYb673zX+j/pfUz9R/yn7OfAD/O/8F6i/8fwp/zf/g9gr+wf7f1X/8j/9ed38+/5PsJ/siUBBfD6DT7H3ungKYHkwPP+fThcO9vT2RgRgXzQc7dVpCwboK0ufKMzCdPHhrL5I2WW3p2ZDT9JKFvidF1fMzPZ97bxgYNMpxNonlIK5hIWinRaIucDVMlKfoMiqYDVkttnqrr1KiKZ4sC+LSqGtoxikIhbEw8PYicov59PoK8g62/cNeha0qvIOtv3DT6eqNF0XwEWcp2mnE78wdxIckCtkVGu+WxZI2Os1OfV0YyuzbX/3dG808GmB3aqVFn8DewyU2AEx9JqkDrx6Sej91XN3g90c3mLZlG5+Hn4DCmoHDLzC+JckxRoqujjZTX2nwMRDjCZGU7bPiHp3EnjZwRAn9yC01wkR6hCd6xQILNwoKXmfngsRNn5WuP1hzjJPOErraOO5Bm92FhrAwOaKeFEvHrH9YTwv7d7ek2KOk/g8CGfoLosSTl+S+QzABfKnTD1nCvAPhe7o8G2NsneRXx5Wifziw0LEKxPskZXmOjlFl1xEhMZdPRWVIyWgM31mfqGHHdAHXoFPMyCYpKHZPLBfTT18SAtwv/3WlMBDrDo1qIYQQVUT0UTzyzsxhrQbD9erXFstF9A06/713YdHASSLCE7brM/r++nyLigPu7A9zhQ1Zy+XF/5K1bkGile6DDI9Ec2A3IS5kfGyzZrfYrxJeqJuHIoemuXU/fet+STPuRTFmtAbvc5xRTBESFfUZ0qWGLaLcqQtfbWFX7AdHpApzFAUVK7Dy13qVxEx+Bgf2D3BDz0RQaicQaP81a40lG/uCTGEy6iktQfnm9v8z/TtM+RjFIBpDQSKVfAXhN+5wzwg2UkgVWZ7cFSEHwuz1mkXu6vQXkC5cyZtCvTp0mbhcqLYT7IzH+X5W5KgLTWlUDvZlrFsr2yT1C00b6T12l32MBfUv5GsFlrNJoUcHF8w31opu19+xz2ztqS2YKVCFEt+6QphVDLf9oayefHaqpPOZmAqe6JIHoK/9n2FVgvDR+uFbwtqGRidHdfcXE2hZeIpFQfikFtLyme3XLWxlSecnbOHBqmXrPbmdRpY/3F/7V7TqEmq+P5/bC/eA+/UlwiXpgLN+whlKIWsv+TguqIWdIc7bGtYf5XE4nnAj6FeajqpqDu+2K8t0q87iSmBISiCn5bPmOolpHsC0oejDmil+/i9JFS/yC+H0Gn2fluNk3SQ0z+vupXtpDjMLmEafWBBZSuhwEWvxfAK8c/PccXjw9m7u7E0yv8aUzLboqVRburG8Q1ZabSYusgP9wdzSeS/XZ6QWWGBChTYKMK34xXdN7Vwp/k4cOyQ5op2ajVdRPn60Keps3XigB8QJCLRMKm4BM3RnwDZES2jJQsK1qhWF/3BoBCSMA5N95+AT8UBvVW0oj1fo/eOQv1tdG3ESOE5wG+ZV3PfZYBsGotkvxuJnkTxlOPIMzUh/EqfNLmsA9Hvz0hWyhuRropwoeIZR2vxuQm6DmZppzL5QVkV0vsKvzDGo9W/oxhM45B35DZ35OC6ohaF8BFm6oACP/UuXRAv2bfGYuvHWcf4keocG9ZvkwTSWkJq7tANLeu5b6aA+Nl0SPJhq9kTdZps2MBQHZga24l9qLSebAakpeWeUSeKTjdUo/ehkC0OHTGGpI9fg1E8D9VFh43/cbA28If5OC6mhG4RRS1NTkUg04mxH1mPE78435OC6mXnzSrwrrzoTbYU5xVzbOEkhatSP4EwFb9BNpN04NwwSloklzNgqQE3CGM8ELX7cVWMHsQ1JZq7OmBRoqSbfm+ksc9bpw7yQXUvjuZa7I+2LoEXMvKM+4UD3EZflAGdxENa3NAA1RMXR2H3fS29ubhdMZLicyC4XS/ck08uTWlTeWL5JRJqpMd3r7i2ojTwCdqtmGEsxSHxlYfcPKu8sXw+g0+x97p5YvklMCPoNPsfe6eWL4fQcmenHGzhtOU7TTid+cb8JNzLsToNPsfe6eWL4fQafY+908sXw+g0+x97qpFSth0VAgyvM1QG4+x8nZaLWRyB3UQMPvoT7H/SIkrOa5mDX/NO4YNukLoYQWU6W/A3NONk1Cno/kF8PoNPsfe6eWL4fQafY+99HU3li+H0Gn2PvdLUlyKVdbWbMLltBiFH7ZOzMy+iWd0Dn4jJh/UEXz3JGnTk5IS8lEJ9ChDlTeWL4fQafY+908sXw+g0+x97p5Yvh9Bp9P3zWL8iru/7IGrOKOh65wm12Jx5BiWeVo8KQxC3pcizeYjxuYmarv4O8Kt8sIj6sMUhrgYYG0bBOt9FvPYBifBjRA0t44yLJdkeI14SDXE4RHDorgbnbyjF2cqE3103BFnQ0+x97p5Yvh9Bp9lN7bunfMATguqIWhfARZtlBeeCBFob3VKSxfuL9kJcU4zJ2qq7YiDyR3kNNp6qokpdW+/1IBE48UpXD3oJhSvL9MvDJ0bRxewG4nrrpi09CczWBFybbmrx+JWHMIuixZ0NPsfe6eWL4fQafY1FqUudRp1q9m4eZuvD307+gM4vmQXKZxRpVFYSx9Bp9j73TyxfD6DT7GQgng0PMILDGhgto3XJj7Iiaz/794KMBNCZK4gmBb3RHyVThWUQd9TliCfqs5Cr7QoeKTMlyt1bLSkLvgTWgD72aKI07dgL5/vsmJ0segbM7v1+pL3g+ho7rQrb4DzCv8D1PhTenccvLmp4G7R1j9Mp4uMIexSTmRbGLaTpl/XzCRfLpvkA/g/eRsH/kdfzb83HpYbSFSczIn4//L7HHFSnP7wAf/ydJaVzageOIXBg/cs7/obZ4uG75OZXPoeYnKk3vhpxCZczPDHxZ+cCk2HkDNVz2P/w3d6v7RRd7atJNB+KJ2uT8Bh+TtKidFeRT6GY6HKCKq8f4paDRCKXHIsNFhss4IzL6KLfLlpthHn5v80AeEBHVAMbgN1wBwWm3CNWkNCQ5/rw0USbb/MQshmbksPpLJbQKh2IUzKiLQCT+hUYEx+g2GrGVXIEi+3ev7BRS/RSwpTOw/PFzUVvYTaHij/BeCEVsNbGVDsBIYNjMtkBjbHFCT5MtJEMbTfug11mbd00kcBh8qhfDTRu6ULlzx2nVZ9W8lE303Q5FqUlXfAATjTz3u0fov1+6/xFaIIn7jB4K749ulSBxmgev2eHWtrCB9A16ADXQ11891E1Pj88/3VELQvgI7ug0FDIVwfem3nuAz5haF7U2nKdppw//n4/D4NFaXx/10QuKNJCZ+4D1eIlWkBrmu/2oujCudrlKuY+BeccV1tIdtOSbu7xM4C1mpLGAl4C/aUbl8cB+DFDFmy8Wm2GLVLHZoNfwLJpqNHrCfNnvzlrRt3WZ01qeqeHFILg7gPkvYzDkNineKOhlfdla6Tm6rgvuf33cu7MdGWRMaatRdbUh3jZ9Tfva/YCPIuGmORsP6ZGlN5Er/KcHzsGDrRZc3w0hV9HSzbhlrvgXjxGXN1Vvb0JI8eEkTe5rOF2cUZIybNO40NB0LIGqnxNo6guVGdHg5t9PcgbPEeXvHA4Txh/3gpPI5waGoN7cctvEkvX/JHmw1Wt7CkHHVbhZuQ+YoiQ9TiPCZyj5tJXc90w4Crve60852lA/XGl+bd1PW6PlgBT7pAHqlQ/sKlehl45jcF3A8xxMA07X+Z4gIY9JPY7n6xiZVND+7BGx9MfUvXYY+uIkDfJLKekhuynXrex82YO50Xc+zJBHfhlSoQAnQTnPzt3Jl9UFPHkvNmmD0jlDCRlL7Kxghxv2wNvCH+TgupnOBdQVZQXRYtZTzIKaTUcMAqw4x7oxK/zurIVGtjKlMJPcBnyiIz2ptD0w+ODDEZNyhS1C+AldMwm7fbHqBquF6mCJodU38gg8/urvPZS+54d64QLNHLNs9kiX2MyOgmUBtBFdBF7ZxJoRDzloOnbK9OxEbOLIr/zgKtKQmWmyJeCKY/vAUeA7thz3lZk1CCA6c5RPmD9knqJbJ3lYhV5NrJeWxctWNcOOU+wWTQmE/iPGVpQaIfuiSDuQ/oP5OPVjIUp/yLbeHnmdPVO9bmZnyps+lUrWLd2+hRoMsq8cmx5UbavBsnP7wYDj5eNby2AmlAPZCRLroQQjkIbF3Ri9Hd+q8BnuTqB8XFckwb+V0SRocixkoKw/VZ4w6KhLx0tzBisFfMHOypA5nlK2QSn2CDfCB/n3ZWmreg0+x97p5ZCnXvZB2vkjpLX4bc52cj+Z/nu0JxxMkd5IZ1KQ2h4pBbsZUphOR/M+USc6OnEQciq0qbyxfD6DT7H3YAAD+8RQAAAlHj1dm/ZTEPcz24GOplFrXuowJJVWHpoJ4W8bQB/5QvP8DRiyKLWqdHDTIS7SYn3geUGbNY+fSh7eN1q+Dx+uBSoa3sUTARSK0Mg/TImTY4oPP4O5NfmMop3Vnv6TTDPOc4HlUtxSsqTu/hKEjYRxUFkgKC62NvTNj1m2GagFB4chDMX5UQULt6kuFnOycyesjHqOQ06TI43k80+wyccadxSkvV7KHLll9gU9B0LIpciQtpPUxo/IwMZfZd6y2aKMa3WEaKXN7uDbfHrXBhtOVgwi054529/AqmfSavk+3OYuA1q0s3bb1K6YMvV53wueosf5VU3fgxgc5DNM7yyIf01o9XWciCvXXZHrIVc+wpX3zWv8VFUDYHbelfthbj8ksZDs/5kNCPoAYPRmSG+vAkqU/EDJTblY4oSX6FVkkmFzgfyi3TH8haGIZIke6jnR8DaDxpvA1x1KgyF2CbcttC9XHwPi0kiWf5cbZkk6++fuxySLZxg/mh0f7aot0xH+CXuieHpjTX/Ebf9rLrDkJ+a8IK8seKwPP4V/LVOQ6Y9mKUKqzdxvNcjwvb6y8s2kelsUOlYWQ1jyFC7CmX+fQ21up4wuKRcVOLZ4FGy+kB3+Uz2d3/nSGkNQiAiKw8WXcQIlwUu9XGe9Ui/up0A6ZbnDbubcu+JMEiNgUg1ZP1yYxXGiZRQe5UwrCKwVSU2657V3fHNnTaUBXEPDBZTwQk1zGCuHNf5JxM/wa7xHYMQ67qqO+jj3iF4mYJValpuUNYd81U/5+WHpMCpwakk43YWw2cSnPbG6KbzRg8P1ktvnKIe1CNQMZEcC11WY+Igo1beI4AfaMNVVj5S635kOZnqynHjzX5yJOtNodRCV10pRz3bcVj+1FKZpU+pn9yOXwSujKVB87mTQuH6leyLPFFMAqchp8Rf2hzeVHCXa5565sAAmuYCVqqYVhC6kxQ0xctTjuELwTkgshkiV6Qe9GKwjkKli2YQQSfzTaFRo87AuRIYigYWiESNW1K2pIeEHsoXEtVM1G98ctp4BRbSh3tSZ291ik+JgRqzbmmqSB0VC//k7mCQiYAzXiExKzP9z442x9tfovlIFe2aAXSdxZuLL279UaTvAMNAjB//ZsMxuxvdik8IqzsxuLLKz6m5Hfra0rQUD0W/4hEivxXziemu+iOw/xBgNs5vWRRRvQsiQ+X8UtT6bR6+c2CdTt/yzqgf1F7wjQlNUxiXO/HKZTzQA1tPpRDV7FvkGDO6n+wOFZvCNzI+j8cfTptms6QOWDiEceIEpyueBdLPhyQOEbOllt/aZAFIg3mYcgfPKPHJfyan7LUguwRJ04ZIL7s6risnqAocok5XkzaFCw75d+IXN2Dqd5m4o0pMGmvzcR3OvXlR93dlQZzGjqt074jFiDn+7Gw6BVkOZaFud/xiO3LbpIvxUB05CnUgXxU/gBjiAphfplpvGCG6hsPGn/YVY03QHXyupJz/x8zQw3G437Z5gl9BGDrKdEIYCnJ2qDArFncUQ3ujxpdcJBK8FffsvWInuEl/Ac8h8cnLWlVCQeDFerHgCFY+5owcVqyis8CrAWkQK7dsX1DL26BkYqF1H1jACFYfIVc8q8ADHQd1x4BjOfcpTMIpccSRqNaY/IEq2hhAXdSlYLYXF3OTS/yOvmP7u0ows3ypjq+3Fj2aFw3ABCP7RARZwtmsEnK2ea5yFKNhvFe9a8+ueD+krcP8kMrE9IxSwOHzWE7hHAktXEj4zuV5Qh0LOE3gKd07bEQyxU+1LElGNvrGGCJVoMgFRt4vy94/ByBu7KG4L4zWwCBgtcupweqQYYIu3exYOjdxgL8dJp0rBAvvCcUCCpbyBRO874MxrGFk4BDA9Nb4Jie+RfzlSN0OAZmKOJ1u39vbadRuwKUCY++rmwg0Ctg5N5jqXLOMLSjQmyMTfAjrrzg4Axot0KGrpY7roftk5pAZn7l4/u2M8+wRa7cPFjZ8X2eCCYzrpcUAYr+gGeSF6Rz/JnsCrntAAAAAAAHvfADGo6uKApFhUXjI5UPOcOUGjnxuX2eVX9RicnDUYnU8UUccGX+6SW/WLRZQ7IPMeHNzeatSDJafnW2zJqWJjrMz3l32vZdfwC2GA38XmLY5VJVzx8exIXBHlRYuBY9mwhht+kw9oJtEhTeBbGYkiwrWKeZrQe6/aQ4nNm0AAAAXwOqtRcJWYGQ5KlGhB9Ag9C31vgBbH0UcBCWWa4ZFfl+1txs5LzNq3LAI18dqAnyTWR6DSL2pUFiG06lanxPD1+FD9Yz0w7pj9K3dvawaQVczBL+zs4VAoKJiJc94TCGvcyLa0L/yS19FeK6jFBeCH9oFCRoYGwP2IcuxmlZ6ZF//DFNmf143XUae2df4SX9bERc5oLU+GxeKbxJDYpATt04LxsXthPWOdm9F+e1AOSLpLmG8ECYSDk6WEAnmXDIg1P7465RMUpWePNd4vLePpJT6Z6JGyTtJbBIujpM4MD9CE7R12RlMXiYtsoMMeucUstpp6DlS7OlbiWrn/hvrTePF2PlwL5AEib5APfNR7rO17OSaKwMiVfiJygH3ijnz5aFAJCHQ6k0+RP2dFlTJifgFRqQm9eXWIzRoPwywCvMsHe/4WBdXM+bNbVkwX7F8wEBnU6JfsfAwSZ/jKEKfzIqjEC2ptHy4OSQjYK0Qlamq+15WwB7WJW/UXSKUoBVRpbVyYNj8DzmQeh5EdkaMlfJK7wZfWJgU/9Zc7ai3r0lt1YLD7G/vImG3vhI2uq9jej3MiYSJ7fqJPyNjSRhcizVN/2hbD0d0hI4N06JU1jo9gp28AyIa/zSuScBIUpDjQNmFuVxDqvtBtjBU7Pi5zAqnlT9m9ki4ul47hevTPJMnsYcT1kRkaeLp1VBm6YKKsJSmME7xqegL0oVj46UD7WwEyZzy45OVIM1nz9rg2gXOjuxQMEX1BP4+g1IUYnbasb92vRcxiipsGzMUtHY4Ls8J6zMvnBFN2qp2dTXuZ6x9ElMdw9BEvp8ty1C0+gvIUVacAdg0963KNSmsu6vKdhbLiRQN5H4XDoAVYYtynclGA0PRzZZnFdUkGdY16ee4rmX+q536yWAlR/xvTPxNA4Ch/92Jju7CEA1HmVNk7qYx1qJdw4q/OavRaNsAofEW5EEsra2htYWi9GH7d/Wt/wmfi8t32j8N8zWnakXwV7OcK1Rp0hZaO3S2uuagY62bUWOMfu/vfe7Hm2ToqxR74MfPxGiLLwN+x/rrJbL38B2JOwHSdlHm/cyv6r9v/KrF5NvCrXWh6XZauYf0ovaGo45gjxJA+k2TMdeWp0iO6b0G6zwLxE/DI9j3ZYOevMOj7viDkswbs0rhQZbUu7zkJLAosKUZ6BxMOMRSy8LGb/RlTxtbyy/njk33qEDmWB8ZaBDBah38EbxeB+LcS3GrvUAIqmjUZx9CsrY9uNthKDm/1UiPVb7N1kUM5fshwIy/ngK8eL8cN3+9QBfsbovkNm+ldtxASsmD/RPHBDf76zzLvFNiB9gKyNrNCBEp47JYfNt/wwVo1DhJLN73wdkHtQ/GwH7BgGpFbMl2U6ArPgKhajPJq1EyWeHeRF+02olYywLGRydp1agdaaD7nbn1tJzLTRjkr+h1OIg+UVV3WW9Cbz5uTtnhGiuuYnnTgUzpao37PZ5ne8S2TcAOwALJ89MORIy7FEX3e6xYOsR9W+0iNfdJgcWslv5o9YtPtskcCHfy7qcWpB9sp+oRL45Qm4f51++txzvJp8Jwssmen/SH0CBgKSekcE9GXeNwWG42LV2X82L5xgWKU6qbT5SixiA+cUdkvc359zY4mGboyrFcTiFrWVl+hvOypa0dMTyO2f/eMuj04HwNROjBtD1zkxH6BtYJP7R68UH6p58hRai0RlfXnJpy81kt+t8T6w5hzHL3y/oxImeY+MxTxZ1QuZ0zrGzLAHpcYy6suOZQiCEDAWaSAmc/nxAWwKCvbUy7rNYI58wnAO1HPtbDxN+PNStmPRCM7cqBHS01Evo9SlZooMii6Tt7Iz8+EHnoCaTmA+7NRQJPGQeUm/xDxbS1BAtN0Ym+eEoTc/6jF0FUlP3fh6QGKo3JG7lFyu6gAxM7Szwqq34wMnOFvhG7RKSvHhwrdi1+8HWGFO63VdSzfrFqUe62PDaqfDNVrdIywbbRqr8IRabqVZxu1S037jotT0aL9tZ6kiyUcE2i6nGQ1K30BT4r+eFdMcgCC+2SWpNxRyBkt+bKQvm45rU9lLPhiNtMXBNabzC4s8BrnZzT7RhrMCH/msMFd8K9IGu6eOewhL8TXZxeenuhKjM44DwWEa9oeZvszTYho4rFTYxbdPdu/xoyYH4Yb9p7ib2OrU3COVWXQoArarbHTcIxOTpSzh06LnBvMdo/HSQuprQSGHyTVQqK/BUXjEwUF/d29jmRmZkLe1Qipy3q7L/bC+9Fj5St5LTeth9TgX6li362Dv3tgfL5uRfzE4Qu69Wjog5Rzr6f4UgOtCHI/PCCWnuDbqJxfJptIgqUF3Ba43HxiMk7ViPVzpsqZh3G7hFknktyir5311JKTLwp929L+qv29nnqA3CQYKNrfWX4+RrbRmGkK9v2y3FqKsxu7vuYGJCyJflzRkM0A7ldvoJyQYiEJ2fnmrNq3gdAEbhGZ0DS1dilsIiEGzy2fDYhlZgB0B1jlOwJ9p4BiRIrYapxUQL8p2Q48MywbtlFz5OWWpVVulsN435/7y18vQRyDAfmFpEnW4DRzSwQNSk7TfXYv+5WbyS8uOgIVNZzsBoavmTSgvEVkwzxbhdoGHVosG1/WpCaWlH4UH0Y8dLybbOnKJ8EK/LClgmLBs4bJRLhsm6rAh4msu4S9iCN4bYpzfY1QCsgBI1M68twmhAFiWJcUxcUqIDDQVXLzzH8fCImlA9agy/EpXSW/u2MZvVVy3WKT4zvGNRXeU9fi3q0pKWwrAKVK2bhu5/zPuCJjNQ2pHH5nPb2OrByztFrP/VLx46ahTHPFseuHEH27vyY+BcdeXdPZlfc6XRCkgysRfMgvytJDi4E3A79DxuiB8e3tezbcrxdeX9CUS3IDxURZeeSqQBdqsP9lCM+U0Vjz/ZArIQFpA1z/82eUoDOa/4qBivmw07UUQ5zvT3igDEHQfDlvQOixjIXSMSfTQfc9UFNNQh5XRQAteshwWR2UhtQnWmYMtkLnZ9XhplIzfP1IJD5OA6om4K6nrEzmfiIwZKHN8h3k3EtMeguE0F5ntIKGzM0YBLP5FOxH7iC6i4IwkilYya5P/G9lcgHHrHe598IDOemGlOO9SbrAgid5VuZQSbzSa8PK+F0pzvDHS0TnsP8uvot0mzfjcH/hCTzfU6Xp0NYFDP0ayf0cnti8JzH+baf3SFXM7zaqV/JJ2rxtHbHJ0oNQDhVw6kxx2yIDgqHd/yIXSt0uF7ZBQJ6Ki5jOybSa5Gj/Amx/zN7FYTmDmq5FOOoWKe8hKVF8YRRct0TR9V2dFwNEsXv7yT7YheHrQcIK9nRefUcG8eQ1VRGcX0eE5wHOzIch9E9gLeznhpgKq/Nq8hGcygbnVjdvQksmn9VXIWK9GFnfeBmtMV8PzyPGiWB4KdM0Y5SbgNIjH2PR18fMGvrhRVxMXdttSm3Xy81OlfdVzoUIPt8OeUBO7JMXEpFLfqabmUiQhlVEX/AWpkyt4hOYhVYQrdozEVDhh1wge/b0qBKVxQe3hJRBzWE/IM0cHKVyt8takZFgJTHXz+tyaznIOfqShrgbQvw7T3EApAKSG1KhkYO6Rd7EbKIpO+hEwHBxW2s+6cycafPGgdIMUBY4YiOCqEGuCpm/Ml39ue37zstveMeTESPkENOjCKu8ZFkmf69kgTiJ1VShFpWiOEjP4QLReFR7hCQjspub62ScvXPEPiPkWRl5lZSJgMad+QdwGzUi+WX5nxcAb+1t0IgT7sxIANoH0hPBPp1nUeAUVNSy9wzZuh10JiEfqa9gOmABsqT4SQkXERWRyqlYyf2HOjAjufFWMNJ56TWV4O/UsYJE05EE3gmQKZcHCD1OvPNgjw7MWy3x4WZn9TQHUxE++6yvH/BrdoUCd2t9/rbBKyY8LgNFOu4YDgr1cgrgRW2Tds52444Uypb2TcygWRVcrliHMKeFN00c5jmCrl44iWoEEMYXsoWTVgvE82Fp9ACE+XCHq4d3uBW0SQKwr0x1NuuAPhbYn6OhDY2T76Xk2qvzuZ+4Fbsupeqzy6ZGnd9Lge0BXmrJyk4/lpxCF2skS/TX6czf78Xb2zxeN+s/J+DlPt/lp/DRK2Mxg64e0+CHy7lgwt0r/wwrsRUd1VqmuXQ+hwPcw2e3fRtGCIQUUg/O7TYlOca12haBl3rQKjKc4s+Or+rlLtkxUqenEPTQS24rXft+SXwwuG0Qy10MMRrdcT5zam/IvSNDqnzk3RwkHlMcrlnLRa/wyDtDXiqPPHY3c5Szg7ydUM4j2BjyeJE/hnZj7rZXOarqNL4Yi1B0GqZU5/Az4llmJYXRXTt7Jz8EVbhgofPVem6iongLfilvbw+d2z6IRN4/tWeCx7vYGPd7keQxYBp4ZfHQuL6JvjZhuvwX4XgTCqaKpboOxrf0bNwe6CwGnv1q/q2E4Z6IqcflanVwhDWFxbOd92CcksYHzStso/J14enYuMjMcDpbM/x3jUVuAHJvOOMQ7YqNZiTXwMpNxEWV5eEVOH+dsJK02IPtxe36r9Hhp1KXWc7sC69qWydjk1QjAi8dk1pkd9d8xllRvw1mokPNIox1thtKxWeHUWQDdiqQ9ttSVTzfs8EBtrq0wHXUjcrje65eubT/SmT+n864A6Trb01O4ns4pZ1rt9SMgKzukrO4wU1AYKu2BKuARwAACUOXVGtSeVxw3q7CLTZNdP9ZApP/Aljf6NYiPs4z9eBTFJ/jtUVAuHSPbbpRTa2cNmhlH4Wp3QTxOXovS6FpSlsbSiNAE2EA7+z52O3mMMlMWGugEqUbh70se2iEzVReE4HvPPdWgCekkcg8ihBZYQfnJcQ7hb/TwCACZF12xNYS1Oz/hwAACX9AuvlDqSeyPRqrBcDRgIOv6x9KeyRGxGofWpfaQzZubri5lickRzPP0oZiEFFNEtSgjpgGEJcbGpjMCaZRhzfbdP6/bsHFJlbBk3kXZZMCqOTRWqnbj7UPBnvuTORt/L73DgIbwV8U+3GdahxyNnlTWkoineUPoAjj1xKtNvX+3OrsxtsjrKkCISjmnj+zGm9P+cLusgj1amGeY7Vawm/rviKRV2/diBr3F6H//0wzniiSzNQ7l4/Rfd6343AqKs39BNb2L1DljJKo4JmoWH8/of+Sgx9LXYt3KEmO2onIVNldAIp/x6tpgN2PuuP9wIQRd4Fhwi4MdFprZeA76u1PzjfGgqg3psNID9Sm4Qej3Vwg4TeaYD4aonaIWkOr4FpgoIY6lIkADkCloc1Qcoj5g6aVrZjrLSCIN6RaqCI8n1rtOuW+TKyVTGL7sQPvM1N3PUcEsHRAJfJhYeQIA9SvO+7NZWrh0bEVYDBHMoUsVxDLwRocEg1sD5AT+wr4MxYUdy/DdDrIczyfUivjZF3W0jOrkqJRSUzTLPDtabIs5SwgYmjhDbpm//bEwFaojv4ksGn3A7xgHw6twhl8kEFEAZN6xn5m8FaFShg5TDh2XBnfY2U4g3mcQSyN0xjvUIC2fDUPvYqBZaQOuXYPt99mqwLrnFpKFLOt+h6SyJg+3425ov49rEVWnG7SEx4X+k3jcogg3CcOKQDfDhXXGrKD7ubG+PEHbRASGJErwyxXPLoc0KruOnSswhQargOaslhJRqmX2FwXsIeGuIsO9+8LGwxqd7y0cCh4afR2r3JJyTehgd+IUFLkG3q1FbapAWjSoXj04eam9VKm7m4z0G3lmAH6qVMHoVToNHzgD+dbQunkhnOaiU54p7vau/eanR0xWxvN4CeV+2n491tCd8ftm5r99l2zRPW2RJ0zJfR6s3RTVyohh4Thfg+oiWM01m3kkx0LVWe7IivDO0Yy/1yo9eE4i6GyQt2wCnYYVmBHpyGB9nMXOaZP2A/v854RYxAa5aGXGzTtWnk+jFN8kHl/ZHna+rSTVhfs7hqpnIHmVkAq7LHo+mCD1R7Nz29z3REKMfw5ooBv9f4m61bWc87JGvJRnreShRNg5JtWMJRd4JEfXJiDG18a8zGSZq1ov/zCjCc0BIA9Hi+IrtuFLl6EygVjNAaFT2v39VoK+9DwQVpQkDl0m1CVxLGW/jepPTO1FUtcJq6DweuC0XAdlqIdCyJltKDjjKl92C7wjW1FOMFZOyR+hOXswg8EmjD/txO2lxcCq/WcjDJac0LgHLYRO7n5kLPUqGPdFN/4Ahb8yMXQA127WWrvg+96aC/jXTMD3BODAUN+pXfNgO8cjOIhWF6hALEvUM7/2ZJbgbCB4pjeHKe9BhBdA+n8HlAYABxJJG+PnXXRy12xHr7mRmFxKI3imwQhvKheo8Nbm7M+petCYjV7xwK3B7dgt0Jg9+CjdJ596zTZzmUQb+yPWX+SpQRhLxB8C7TIKlK2ca36oY/FMjR0h1lAvVZMXd4ICr5ucfI6O2VfqScvHFaribXrrUXS+2BAzq9A6L6lqtBzEodUmEriAbdFdgob+lG/VXhZFE1/BpAUChryiOdc410dKaFlvjs9zT88L5xflj9pLXaGYoRSRLhQKaqukoOyXfncgZCQ8lBfszVLpnlnSj8WXkk2mUO7xTVNmN7SFLchj8uhOTbfD29lwFj1KHnn+KremB+3gAnpYyovTzjuBFdaj505v+zlnaT7rRT0ku029VN1PAXjvH5uJo7T+VCEU5zaJY6vqf1rCpIyFFlRFgw24SwbiN5zdV/S6VQTL1HFOE7/ZA6g33jzXgzApXy4nVCiUp9xHPV91Xz66Y362DWuqgtSF+3oCSxGEsdX9wSCr53f11+QHgdzRdA9bCcgmLubue8wnpO+cAbAYxUnNXC9Xmbfv7Dukj6JU3IcDBTpv/kI5RMEwe1aYa/noxcCTx/OBYRVdYg+WyuM5/6n+Y74DClcLQRplDAqQ28FaqShwiQ0QfiLWgbedGzlN8I35ZbWqmD5eDOHxm4l2Fvfugkn2Uc7hQ8zNXZPskXcmH3Dc1/UikVIudLY6TluwNdXaNe+EiLgzuCRycLZl5x8qQNh68H0eQzncEejpU1hrz2093gjxPHMZAeH7nsKVElRD5yxE/uzPcBi9jz9v0Ts0PWcTA7Z3fDiaIRsrgrB37NLPISaEd1OHMGbnuFLeqxLtg94sFbHWwg2Z4gj3Iyd5/c8bkWuRZ/4Ri/PvoCdcSJmuR/KboPMw4b9Dts/1g41G+2/ZkVT3sj1vZJCgrJVSqGQffhauF5Y5AZ3TaReJTX1OW7FR9UrzHptNj8F9qEvaWJrQRKyvYincHst0QnSuBbDQWiwfofqESA2D95i82+sYJIf4MrmKyOSWNHsOwZo2ZYgiPTF7GHPDYRCei0LN4BCryc2uEu4Z7T5kG2RvI7WYg9Xv9iS0hqgXi2NMSFIjQEdzDu6E5ten2Pxbogibs/bvfkLCCUVKaXpw1QN0utsiAOlt+TBhT4K4VFrYE7Py/tXgZzXct4Ib8/B/lm5bpcwl3u+Eh3WKWHZWfrpQdmo4fFgFUUPFh9fU6MyiAADklqKWwzTpXd6SB+Gh1dDb7C6RDD829w9CgJ0FjP0rX8OQJiFYWphHYM/nud5MEBvn2xaKr2KveqRBVMjtqMWrMExQtvI0uxrKPhoC2eCytOdKlmu0fGiGluO50GmvLyWit/Xcn/shzmowVZBwUo1hSdCm7CLWI5+GfQnV4+yvzhEn+qL8fzoTppzQup7RrX6JoC5f0gadikgihGU7TCucz5R70TS424Zq/ZaYvUDQwXmFPmDQSHSIAXh3Dwya0wlvIwEzppGT4RFL787U6273NUP+eVm0U3jBWe/LJQ0HUx0AA3lP7HekTVD0QdXd2UKLM0yEC1YJvzqn9ZnWegHGBwfHR21BfOKW8/YVB9+zTUsoKNa1FDxEBiAefFEcheHbIPpAD6IaDh1jgcF8isAXoTIXxEQ7UU5dJKg2SCabtstxlKLAoAhyYNSVfWbjouCHRiG2Orp8Ueims/tvBr3FF0eCa/gWXOw4fltaUvlHqJ7cWrrXvY/K0XVobyklB9PYyzZqVbLhpb4AIUICh1kNKImqdO594K/zaPHB0xp71+mSZS4tbKrrmSeAvxYeMzezgFoXT8sSRkYqLyFo4eO600f3DZvivVy+wOo6Ye0vGBo5t7Cr4Vkokfj75jtdpz4UAukweuerC7LHbquuTuNZgkNdtXdm+vY1yjd+rxemM2s4wld562eZHqwS9kNNkEcvt7xxqKNkBMHkVf7+mu41Vhu9JjN0tLsDtVx+CqM/Tk86Jn/GxIj5PNBI2Wjr48LWBqBFL3TltHw+hZby1bJoOo0X8a+9TKAeDSh3fHjnS3bByflDLYWC5NGi/o0VXIq9OdUjCUJ87o2hbShqc4dO0ZXH5e7OjfRMlEdf9kvULEr55ZFYhipQMYkECC/YlXS9+0tX+Fo0X9/i3pbURfjMSWaEd+zeveHZjm3AG6DP/IlH6p4UTNHcR1sPgr5c61G7XW+kD0V/TImPhdWQttQ5fooaxx0tWZi1SUHHM6XJTDxVD2gd+Yj5D/TRVp+Ku2G9NXW+AnT0HOz2KBFxu/OfGhjiAvPELzvWynLJcvc6vD2xoIFc5cMtPB2k93OpWT9I6xit4NN9SyLXMZpCObH4N8nhkvIbzwkfs3AbnGiqA+6JmUVxh0gUStgTt6iufp6ocBTP8SJVIKBhdWQuAM3HmTxB8uYLd7ARlS3FOeewSlFcalXl5DRkPCzVgRF4WXC/2pYSsj7Fwu9ZzBl6rr8NvsI3iqaYo1Sqrj1gtcZck5Bl3AUJPmOqqQ1d72rj3cg+ynLxL+roD85Xa6w8l9uFGom42Wq7FwfvWY88vn+PYHPMwZxXU5ZrIQGATpjsz0gdpEf/kkxob1j9/hckv53yvXE/aDUk4iyYgBcOdrIJIYrdMt5zX9LUWguuH9z7H+Zv4gEEgo4VLFguphs1TZZXJgIP9TBFBn+18qv4ZklU9qfrAYyo25ESpn8EO0b+wg4T2vSY34dje834XhMnNeh/7E8MKP9bgaRyOXPg3kxZqt5tnFK+22WxkyDRxVpYOJM4d/NNKmx+GnZNeR0yJQ4S9i2FYT6u0xfWkBmynB4lQ7EGVMWvzLNOM83A5YlSHVVsrqY61z21QgostRdysnmW9YlbU6HVDogKJomz7GUo4Yl772N/XTgSGQnTgfCUb8EIZhx1qR+d4xlWljDPUajXRpje0JY11OoUCO5wU4BWTECdHXONQq1MO2JUdd9MPlKBvQDfknUF9WSiPNfPEjlmNqOj83JR3UsIpPtInB759HbrdXcpytBToESBUbP4+ISqCCgpQwUKSIdXx8CtZwwXkwiIM/ipDBnUfG1azSTivvV4pqy9f13l+3BqYjevD29yOo22cip3g/1uW1fjq6QIIV0LebdDYGKAAAAACWi4AAAcuFzBZybxulqVbe0MjxhpitOU0/hZOUsjNSCV9bB0Dw1C/zRKYtfgAAAFrJFOjm+CbVYEMoLp020B0bh9cW7QhAAAAAAAAAAANExX64CoNHvEWWgWaTmenShgZi1dU8PEt7+sp0/HvKlw3V3oyeAAAAAAAAAAo8AAAAiI3k87YXkntX7vc1oWyIVgLNpl6gDZnV9IWLkphax2uuvHk7dLhrFf+YhOdQPyQMq/aKgizS6BoO/G3/k9iN6l/rRQ4ege+J1Su5FpuAAcFzUrDADrv+8UgYWzcpZQ9pUR8COcJU4YHxkF+7901Qg9NVismYOqZ+tijVrqQkJy597gsZNHZRcUNc0iUN6lYU4L/Hqq+6bBW0xxnxdok2EABmoYyGMZNCja2i5EZRLqpEGBh5dVmV3Hh3MKqXgqfdPHtDuUrGSAdKmAshjX07q0nEnTZBJMPXFfZAU47L9W8/tCu9eCz49ZbA6ADYUDBemYGwGM67XjN5r92HAl75+RoOeKY+QQl+lsCxPchHllgwMaYDgmIcrrFwssESp3QHGISFEZCLlWAVK5A5sBt0u7vMd4eWPIGyzWRTR7k4VhBhfye5PQptWeWFemnXDPUQ0FTEDRQ9Z5faWXEhca4ovkfxsRebdo1Q/v39R0rPM8QXEW12Ip316yBtv/m5uU+kYSj2quFxTSN8ErZBDc3rbu4ksk3Y1cjNmBTVdZIB8jYn5dZkZ0fhu9YV5KU7a8dKW+hOBjidtTm9Mvqedg9C7M9RNW8yRgC7cVmGtvaVT9LZZwwAAAAAGGfAAAARwp9nFPIrKAA1AuvweIu0RUGLRPOVCUZKdc+XCUhoGtc+VZow8I2Di112mO0osn/AfPShpy1oph9Zp/+Aw89Akz9Q/AN7omIg019xAqLmThtU3JHYO2Gl70tkG+9DCS/rc5mjW+NcMMbg4afmu0aR0QrrZ+ctXBRwfQ8GBei4lgzJlbGV00jJtwjZMbDB72bZFs1wo+51mbu0GmEhOW5629MQiDWREZ1g51N5dDPAlx2mts4S+/DPG+WP+Gn2SoYo7Er9yhA6GIYEYS5xonu+TRR5j+4GzCibPlEnwWLgOcmtbbo8cwEZgLLgZjc3Vuh/iohZcjqDpcZacm/wf9Lq/67y/zrtI4goN7yNd2QvyrR/8EMQfzsdGwHrYk/x/V2vU1q/4KipHyJJV7fwXpPLg/+UxtPhSC225f9+Xmx2UqtuyH1ItSXqlwRiSj0N6qLXv3KDfg8nvaE9kAAAAAAAAAAqDeK34Xs5XwJ7Wn3w9sXJg7JsJpgmnY3eekF0XXejp5m4Qurr7W+b9nuBJSRfv53WqEbfb9TbJGXLgATu21+N2Y5+bHFz7S8Jbuh1/3fIJimjBtOonoJ71wF1DyVDSCdgqxAsIZog3tWJQrKoZz/0FZ2ZU0Jzk3MF8eq1AuSI0NBAHD1Q4qy0KYoJZv3cRk6zc/lhrvZvMiNiOilewrz8sMsXlJm/owBU6b+FIv+QICTobQgLUbW7pwdgYNh2t+4w6S9+SjM2AEzwD3TwUdQlSHZEq/3TK0nG2iYYJNEb7QMsKdgcxG8kSb6b4FSWSbaT3YtExy8jMEyo7jzwTq/iMVV3q51ItWw4RV0j8BEFKoUyCtEsO2e44+EU8tySU+MV2gtvl+TjKdM1ba43pGRIssxfI8mg0PbKDJ8Nu4XKTChrUM6uPCYwVpf+lNLPzhdEj6AVUXbIOwYZtl6/FBmhEti3h989jWrytV5s1x4ehJhsLyXc/XOn19LOnP4QWL5PoarXlfr5va+Tt8n/Kwf14YH1bwOli5nbeAzIPoBAVzHj29zcIqcyNsNJngamcNzhOQ+p/GUjjNtVO7yCcMSgKV0iLVUXTjcwXdJaTJcM8gvxsX8uvEZIEXVjOEJeWnYfoYfzO+yXv0JCHMu4uCvG5b9Dd5gPbMT+EQaNZtajQXaRyOfAZfSqWOZHuUN2hURWCOKdl8y0l7pSfNs6PMe/qe/1AvHrCa3BsQ0U8xms2KE/BBBfaONGRjf4acu8KuxxMezBPKO0jA0LikHY+UfOJtrBmQYRpCQO+ySliAhUWIRTZJJ6h/DRlC7dmnX3irrr8I93qQHXmC7ikUYOAJe+dKFVIdJZ45y7NulbrdsFoNJONZXP/f1tG7lV6zaJY52LCB4HlxCSNQ+aYOd2CAozpcsVRMo08XmvajAINBj6tADCUCbQG+iOmrxWmhj5vGGzi1jhedwAtBvndat31JXmlk4K/ybafYpomEe9kVySUZaCZJ8t9z1hCwX8uViTOhq2UmPJ5uClHSKsFAAAAAAA4uO9g6jjggb9Fah7X+7cOLqIyuKKZAPPniBBw737vrm1/9G2N4b3k19vGD/3TQ6y0Jhn80TflWrzxuM9j4g7N6vFc2ts81J6sSiY3xam3zNqcrEVb9jv1BmA8wdfQE8YQS4CaApim4QcJ13sRSdpCziOfIp7uDYJcg/SjOjtdLMyeu8TCUawW49nK9bsEJDD2GkRByww8tQM9N4YoK36IYD80cAhmVavKgYqvVPQysZFTtjzq1ebFY83XphLZ0GrLeM3B4KHOOYhnCsA8TlTtQWm1FTLryLTeK6gOqijCVEfJdjNqJ+9kwIB9CwxX9lFJiBZ15NzSD3G42jVbEX+ZIqSbBIByMQXsuRt6uVso7E5pMql06q8B9W1tJ8hjxsU7KK96SiFEA4QWk1XSrVaNzc6QKgYPOuS1SJDzJKDg3FFM9C23M4QzEGAAAABeHz446WG/Ii9WSWVcO7GcQckasWvTt3MDTA0wNMBHChc/+wZ+m/ncmwTrMseisaKda0S9Tk9mulgKODd2mpcQag3BN6wJWXK76QWppBJqE1+z3IyoCSVZAAAAEGiMrDaUUMCQ978UdxNT7m7789oLF3Ar5JqdL1uENqUF3GNXRNhiMPRBN3ZypLSFzrHydRvCkIPpNy3CROpl5Hs0mnZRZlLgzvGfBGlw0xUOua9PFO2dxJN1YPDDJaiIUZHe4NuD4ptFpS+GRB0zm/vVhO1jhx06VitpJzuW+A06XlNwSZgnLTGr6cN9fTjYI87nmheI01jhizhMSMwqvPBsZaRPpqayvt0avStRE3loQgIWEF7xm3K9vGogDIpFOl9PlVj6PKWS9TTWWuxhK5aarxhHgcOSK/FT+LoTdwJR5GQeYX2bp2Rcy8fJGxyr+MFul0iTgnjVRi7dCLXPxuIREQmn+USI3eBNKcJL3ww2Kpa56FFXq1DifgvaG3MSRRYrzqgfHrl8VL8fQEwuFu2NcboAJtvEfBFsL95ZhHwzHLceuEBSZexCsN9Md+W+4qvQR7JsXvyx8bVSNgu0BXax8iL9l4a3fcaj959IpDGg94dU2ZcgcaH4mxA6tM7fZKxyJhrzUOjNlJNTGWhEKHMRIqZoj5nBEy0AqmTHcFw7WL8OSaXkPH0MLqy60OPiIMS6rtdj+uccHoemXsGjyx3XaY0P9/EFkqdefXwfoxz+BRgn0RLDK4JhSPPaRbB/EjiumElOWfa8ArGBEvcm1iAUnnJFvfMGL9zqyij5/cYoUiYXm+aPDncZkJdu//7dDgyaGDba+vp0SpvzXV9r0rQYa6UA/E/xgNKGHmNtK6xhu5qnNDMPsWPA+Z77FfhHn8itv5CksOD6M7gsGm+/4ddgzO9A1mysR3C/d6jewI5U14G+TSLBd65BkuynBkJQAZPBjlQLr83j4Q6AsAyU3iQkuTH4spcUh7O/hlNCRYFEaAONN2lj156V+vIJiGSH8FdeX+o5xp22yGGiPs3zr8uhKf9bzWPUZ61c64CfHf8kQjVYoT5NDnQUR9oeLkoSn4B3iB80anKzT456d0UohkbI96DC4nu9i5+ZtkHNoMcfupxYJkFe8VcLwp3MHoC+wvz1xD0rcHdJWIf7ch6bkQCyCqE98HJoLxRhTTfG6LUnSrOJi3amAnWK1DDsj2EPwXIPDJFiPQ3uSHaK1KyJt2GBpKs6UbA2lvmHZtJSDEfn6kP6SuRraST8bEUqU53cI82aB23CuXiY995Nbh+7TwyWcfUqwU271iIGuISFaLZBrkMLRE1nOAfwTO7vtspYGoOUqV0NtQ9FpBAU+d1e7uZV5GMKjy5pqPg8cvOP6mScdpUMyODjjHUuy7xQ9rcs+4qFGqd7mPrLSOaSOC5Ctb2QjL3F4BkoM1tFdRIghoX/FNOB4/FgdZIoti4zKaCoSfTBx4+WhLGce3tr1wM+CumxY96AEP9kk/y87Igh8IsaRX3OGt3UUwgZVwqHhlmS2+bnW+GIcGQUOYq5P1F+05gkX6ZAjht75vsCZ0IrgEAynME7m05eWQ3IjW/fKTGV/k4hL572j0q1NqaMMPvsMGk9zpW4B/tl1YVycBT7Io/0wDmx0Zsu+fG7Kut2cKTl0q2wK41kGA30R8HBT404qHkr41kJbeJcSNdyQjMYno2zHXoqqws63E03ZlNwDSvOJyLWN+aARjvJmDFx5lCHOQrGRJFH1cpUPtlxo1fyazYU6z65LOyEV76WnbRkOo1Y5Q3iyFZoZjn1FE+d67utGKhE9G0l4RoycHm6gwmy8fRQ7iHEx7W9ynS3dPEO9z4pt5rXzKDK/rUoB2gVWk1OG+sziJKlztWCluXpFFANOF7Zg8XTjgsEsvlFZF/44Ycpf2GOaG6Jos71G+YxTk516hCQ4V6VtUVfbFd4tqd2Aa0V3zBQJ7H5DRwmBx2cCx1WNf4BdBRPnNoMOFAupGmee9m/rsuOtmVZ1Smt4nEXfR34OfJyvDYR82i94/pwtiBo+HaL2O6RHa8YWrbJMtO7K6jTaS4w5MbwclNClKQUoeXJSrAVNePTD8iJYoTnWRTlkCERH6HG9JcsqVHuXpImqh4rOBdNb3ftsEDlhpmMGTZHSmlTrsGaqVu1vSJUHTBzBXxXZt0DUjVCRZ0aF6bFGxTTAU5489771sVstw/jWvaH8Rg0a5MFd3KdZzja9g0Kp8ZjWW5vHhggtQOUsKekI/pmhihlFJdWP+k8wDyX4iKnL8IVYWdZdiJwKNG2AvRLx3QDcQ1VTP0VVqpYD9Ug/zL8sj4FSHySPg47Mv/pnXv+IndT+KQ7f0UZiVeHQrB31q/u5w7bqh3DYofVrbgIk30TDxtGcDJ3FDHqBff0l6irUDrPlzXWldIrcykXSxfuRfxtNOBBipxskAj4IaVsKJWM3+3IoG2Zq+j5bkRuO7Fy8TQV7N5JXwIrk5/jl2C3GAKruyoTQrjFyl09y8PMi+XJfDjxDxKfjRASZyDKISPQVlFzMcTeNVvzwlwRw11UCIkolwIhdGbUoeV5oxYnwkEXU43WYhRKL6/Thcx5BTtap4EGvn+G+nbM6veinoAVbuRlljsn7vqbnFBOtFnnRyBnlBebSCho9yZa7Du3ja9fQEBlfCTdzd/tzp+R2TKAI/Gu5vvgspp/VR0umAu3z83tcJ4AbisB30+ynyZpyW/tq41t9HvUckCecFKXRXaHl1JJfizLsIi2ru08RXx1tzaVwQEWO8B+vsN988kOB/pTQwR0YHQUvBrlPfdeyEib4xtbehXnbwiHDxf8FNMIa/T7iebDoqIRRdr0FuAgaT+reNQoXUDKfFzrp2h75uVbHrh0Mh/zGvrJiJAwxnW/NVL0ZcTsCI6UxPbcs9uDTH4YlfhumOY++Kw8UNW/cmfxUkXP8vKmCvSEHXDEv2b17p+w8ydV38ARf7iiZBNDapyutS60eTSk2bbFo4nrvNtJoVFKd2YCIhh/NGbymIQaKqiIY9c0IzbZkgTa7ec1WGPkJ/PuH6YgaElLX9bUgSIeeMRC0mEjdeeUHLRF0256CsjAJk4Q51jvDbg0xuVuprMMdZc46LZ3ZTWtGgga+0yiHq5qf0htQrYlHxnxFUL1hyYjWWKaTw6Cb6D6t/ywnUFQBUPZWhxoQPs2GjhKiB/Y9PtKmcima7q4Cq+ywqTnmgLKRdlT6R1wf8AS+39QoyAsM5fjOR3Uvy2hKneY5KDoTMYyFSu1tUSsid1FUeOl9DgRCY6RV6Z+5WOTQaZlDrDEjAMG4k+z4AAZTbkuHbt6dkhrKfZOCl64SA+SdL+ofuOw6hncgZyCxjhg4NuiIMFmQ3+Eai2X7Y35Gw2XFAE8SnvfOMUZ4YQZfbcOm6mo8rNtUwH4jRltuk8LLs489F6hns83uFeqq28WlsX4IBNOZERTx1htSJsKIZmGP9KmEkt2rLL2R/PD9T8sxvagJ1RClZ73Wy/yh8B6q1zK19L5wBSj5eXng7NgxcNQCGloNvvyfGQJ4IDhBb4AxPDdX/Q9saDEOQvKpYsS+3fDB74eiNJRp2MIM0VKB5Y28xQCtSxlsuSZUQA+evIEj4rJilbjFneXVKVKKYNiZKxy4zVi7PFxbgQNj3H7zsfOSrH0sA7JiZWrby8n8Q1wz3tY9kLS25njE+MemovD0IasHB1SAqRR8k1vZZVXSnYb63wRA5PoQhEQ+wA6CvrzLGxhEMJk+3n/3IvfzkInmHoH9XHj58cCs5vYQBaTLppUcNmlMtjMy7EywW3AVKrqAVMY7nIZ1y4AqHUXmFE/sADfB8OG+J+jmyWidU0GetoEAiBAa4Xa1+SkyRQOFuk8RvPI9BVSArP4YI7nzIH87Z9w7LZb+MELOrW+t+eXH+BQE4UgeP9xcWgmhsGDqLp3P0GAGrVbYRNXXuhA4a68bM0xgxHF5dHhACUM/Fj0r5menIAiVXmKsMiO2D4bgB016VC7rgYjl0bQSduk1Gq1poSBOiVR0C3KpgpTDpgA8w6h5+51wKtrjlB1MQcyWJj8d8FRmng0blMABLrrHUw6KbiQdUKdVwjAyVdWtR9uR3G8AlqaVwD6kuEV5fDKYyfoCCM4D/9zXtPtbyimZq8p/SkMdkhuraRwsYhy3ZgGQqBJCEkISRkgVfTA+ESetT29ZBvhZ1uu1hFNADSg6/FvtO973KTTLcA08oV3mZr818X86z07sXVCTFGfwTjJ+Ej0z5dSj/H9U2oS4CIIaIE3OYzIIG7H/UxrxBDmVcfojZ3oVzLKFe0BqUTiQ3DSKX96YWup1gqWMwV5iNYT9HYDeJ66b/bNkaWhiC9Y4O7muWSz9wJmSOYDlbZvF+dlwiwAEjIk+UPUaDJhW7EUqbjOuWwtcjbLF5YIK19DSi+usWPTWM10vp81eE7iLhlWnUPtvmbSFTzuJhxukxvi5UUEWJBsL8Z7BRvnZbnhNz59jei3qV7YNpalBCheS6LNcRAJV3fNLkH4Yy4Iv+dqHItyVssMYbRMVRhuI5XLOzZZ0hoQaTuK+q7hvwS5LeS2Y6yww2PDl3dIbwCOUnf9a/jpx8pOQ1S/qBzQe4VAvAAf+UouqSukzTBkXk+mH1kyrg6WDyWAB9748TC+LAN4+hsD3yb74FChf0oNbBa0mG15ETxbvqogUdt2B6PB14RpJrsFkVRGKABLyAwC0LyzosHDnQhjvUr1sL95ZwEs1JKEpY4V91E7pHK9Qc98DomwxDS0GNsL3Y1bmTVymOIIKtopChfQbL5X69CDtVFpazpeGUcmVOlhAoq31T93X4R7ibIAAIaNu/rJacKki3kmHyJ3FD/IzQ7MEeIWQl/dbDfHv7ETfcy70l4lOdpKB2938qnDhKFfkqeZgB3koPo7Jyss+v/oMeSc3UFNnoe6/gbgGcDTV/lWgqm5bKEbsat0lldfe1FlINlnYNF0JV+BtgbXWXmsqi4PACsZ9YinQqVRV/8zBd4eqKWfAT0QGGt2ZNMdMRhZCEK9HexTM38wPpUH0l0V0hs+bhH23fL85qWMnckxhLpXOmi0vkj35rJNC3q0p9lGgzyfkkGzZanPglYddHaMrJc/h+THYJwClxClErJli9twKgwigzONBwbZMcBwFul1R+zEKuzrsLHlGM+MAbXTrCA3KVsw9fuh6Phqu8/SJgT32d8n56weTrfQnlK7UgSS/jmx7uF4yEi3svJnZRN9Ldm1xFh36eSK7JHjOSH8Bfs/rilbrPEVj5aAC+Xym0gCc1KzYyGZFR5Zs5stqFN3dTPeNS9p7VsCtY7dSKDpxBceieQgnHiJZrTaXMd1jYPuidMnwqa6YXmhaQi4tW4FdpoPfSjQHvHVhIwIJ8aem6Hqxqg2jv70pEl3FaVEZHatrUKgVfNW6LbngVWtEEzIoTzUkgL9d1OLxomoegGTyuXdhPdIjE9yz1DpAOCjTjeDCj++7KgKPonLYAuJ9Utpg2JwBIWlurHgcWIYgqrDPz2qeK8nYT69Y9T4UuPcuBCZFK7b7HAmVi0shPlueOQPVOkMCzzAF5WzXUDatPL+xB+exWNS6dbh60FEHG+zPcp0BBIJt/5sRddxX9HoqwQN1U7HqzyXZLUP5C5uSKbQ3x6akoEqwOafzxn1QJNA62ard4r3pMFvkhOxc8jtr87JWuSnCOaajWIpN9ey59HyAMpKDC2R9G5P37ly7aAB8ko+TadIUGKiwKrrXV739vkXc587wGZOXbkltuTt2uID8+0hIQohv6p8fjgB/QtIuZulFoCvGvH/HX1aWQ3fznycEIeOe39vh3q5LPNtvotZGWqGz2I3LGbaroHd8FZp9rcHFjlECIjSaaj9pXoSIdvbnYNnA6D4h5oValB9j6+58MoAITDJkbduIuQIuf/02zLELWESjIZyINQkl4MKAeoO05c/vae/EtpArvX78/a63yMbkfmma9gtpnts+GwOIb8WniqG1Skm4cB3Ybm+BC+7O1yIFHYSGhvXZ4iQCrhNIsLVQP/tfpkU07h4NNwU57R78CoDgyXUwxSeY8UpKwxVUkODu6/KF4jkwAdzmqgsc6Q7y4I4A4FtGtjnRsgTKGvGHQLYF38efaUPIQNL/syashu8Mmaep1qm1Cj10CsIxjxaujJrmYbChU0nbSS5+B2UJG5bZhH9KUrzrfb/RqtzgEx25Id96z85ZPnvryMmbhHw3Vxy+CSGAAORhOM/wBKVYnurQEYbewVQVc5uFh8GdXMSWEHPGyVNUwWGsaJGvYX8ubE71B8PnPGBA2o7Pv7ICSQ3qM9Ja9BS1zgkJguW26XMkBSbC4eM9m3fOWV+R18kEqxWC6K45/CGycn7OzLUNKK8ldRDzYWsAMa6FxiaanlGOcniKCzfTSbMIZsqsfjhnOrOvPWN6eeROu2KDDoTwB7JbtM44A1i76wwL3caJ4T29ZyOHJpsXZs3TopKlBRmRPx8CchQ/VHs1qwodzeGLSJlPtiPlnfPzhrjsDgK+kLpemGDJUdBqlUZsvpWgbvNWx4WoBoXMfepBWyu7XxC0MhEq0tdQrpOwtK1oe6Af7J1D2oGsorFOGppBOVggA9XMWkVgfY2S6tnNqFsc/Q4V/lnD+L8SLHXmcC2zGoPeiB/qrUVKroP5yhgLmG1gkbD31WUwnRnJ5WMzmELKvZTziuBKfuJLktprpvGPg/E+gRIx/HdWHw1VEEVyTphATC6Bp1ZetNrslNbGSOHU1DVo1gdskhSbTH7VGzIreU6BXKmMy3goJqb+ywVfi5K8Arc2SLOEy4ocBZ1NsaBMRsBmulCT4IS5fi0tQGbR4jgoB4/7tR8GWh/Y7YVPsSWtSD/dmYhIfKLnAl+WzIuxBeegrCKytDyp9mrbeNttyPDCy/OyMSPnvAvJ89D/rlinNQXOlrdypv05YyWoxjWXy8AkVywA2WaKnCNHuDo2bK2n8B9fbvmNxNa94BGyLsw2g8I/HswrwSAhq7CNUew1xfUvUEe83bHoYrEfClnBtDzKEoNOQewVrr4Oe7BMNCH/vthT83YW3XG44wtrt30IvNIyr7By34GxqeMRyFTJekCfXs6qrRYhjYcOA/rVH5bSqTU8IsZzxbEsO3CMGORBp089aOTm3WgguD4Gol/DJESx3zhJJUOv9iTdYFkTP1fnL67a2jVlq1N+wlknYGyW9rjtWZgyGV1nKvz0WdXKNac0dYnwhS9FrDvS95SrzVrHwYWndcE9/RcyrinRsoKAC5Ys5JDJFHLjXElR9K+Dls2yHbvAfp6Y21IPVaJ3I7fp2H+6RGNvoA8MYLalmz2tjTPWwUCuqIIMbOuxpfVgeWRf0I7inFlPnrHCo0Mx6qNgM2Kc5e8KaLe30NnjDiBUEKkfWqtYd80JpXHLqoV2z4SowSB1XudBJd2Ch6MVuzj+AmmjfjsP6QYEVbHInmRAN4WG4Zkniijrq5Jj/sP8/ED/s/0a09qMIJQ/koGQI0o/KMTculKp6cZFecTJaReLRA9CB84mdaMFeA5NiN8zBaAIqAa0JmkDQUxJD4kY2pwDZDrf+gDSrm8EiVV1BjJusGIHdiuDtQJWqwNLmeIKdMzePwv3QZjxfHdY2ThNjiFnHYYiTwyshNTMOZquTelvaQnY8MUzN4wp6MSbsc0dN/vJcC5DDygrkx97X0lpZ7F+rFurw8Ewbjs4+/zYb6yLKa5rbspv+a/fPkxWUqGusmi3Nu+182E8WjEIxvbc98A9qpIdqbI8awzQClDiKQDJ/Vn5STu+zmp0KsxGqvf9VlGMFbpZ5WF5f+V9Pl7DlUv0qgvy0o/NmKZONkjmDwtzmgHACJfnEjxxCfLqKGZ5ZtBQ5eQm9vM5Ggpo5/AXf8wjNjpubtuy9KXfBnvrf3kgltd57gACYXq7c2KBvvG9H9Bw30PkxqQd3S7rlUcFYycUyRK9kKYdbZQxUvhjUFZaFwCLuUUAbJ8U0uKJ+WRjQ+kalHpgVSPymo+i8C2Jqg/zXTVYCObJ6w83oIdQlIptBkun5Wnd03Zea2aCXlHUfiwl+CKRkbEYlagHUWGTS056DZE4KEEPFgh29g0vN07hB226AU+KQe/0ePf8A0ITFiMZYN4O0AptGOiuuLgOl2Q9sDNP/n1f6/cVmAfRwMzmCZdOUk7cCv0PQzKLu0bf+sy0QG9d2Gm4IgZRc6a7TwKd1WH2P4gVA9SAsB2xksBjwc//AS/wj/sX6cfefRYWi2vsMxwBpeWKxD99aKjk7h8RESdAAIlGmErQoFWgLWVBmMGW2YYY+SNZDuxH5MINm7XzlKCLPrtpJDqrSDj80nLNF7YMVPximuzg2SwsrNnOgKlJuopzxHj9VpNLpy34wwonoJ1nHtOueOjnb3Xjw2wL2oYI6XfAvzA9evmY6tq261EZ+8E4lACOB7PHh/+KHAxo+d0XRD6+LKsnXzw0CAfO65NJO2U6q0AAAAElz4K/vPnPp577Iqw3R+xfutGiT4RmJmo1vJdCgpuD1Dy7GY0uIgrOfYF2VKRunsOZMYzTRdmEXiG8sik/bHJwE8V8Pe3SPbo129Ayrg8PKbfszaKYHJZl0h+rlqxUJbcg7M97htuR24I5quaddTKUAvIhg99UMgn0KJrikWg0JH11kqp5vLh6U6t7zxL+wImDOdbRwoB+dL8kgZr0cWZa4UJ16XrWOvFi5LlyeC6BuC+6ZY9bgN0L1qHKOMDzOOt1v2VSK6k+rHQ/JNfl57H+nYXbtsXqCiuGYQNym19wm3CKTmluJYZKj3FY6MChSyFoIQzeHG9Jqs8o+3FDqZ3H1lUfMkiRLlUrjdiCk86bGhlNb7hU+ixaVk0TzB5q+mZPQRkI5Gv3uRdbTzpqtFL0p2tRwfPA0qrGkP0cbEaMkxVkcsAUKpF5jV1tMkD67LyCTeFAbk97XhFF0qs2gGN1p54XnqY/+InNqoKX1dmsyZ/S59IY0SrrztckbOz2ql6XxdC8Z4LD3MhPmDoI16A5sN3dgnD69TJA0Af959HvUQVU96MFJUNV0tCsACYndlVPdz/MPf9vVBC9HIdqkqB515n2NJHxgMxqbfWlmaNFbn2F1F6eDvBjd9zrmTI1SAp2kwXNfcHSNFBYTtcskj9dAf5iOQm/Bl9G0tdaFrBrUsAQ+Dhkl1iji3onCWzGjDqUytsRaZ/ouR68tKn2oS2G7d4ONgYR1g0uxvV+3o4pz/LZ0aby92dmza258MJWOzLVj5tM/p94drCa8FOWzvffBBMRUTjAedakjlC9ro0wkqEzqOl0WIRg3e+JFwK0rJYo5VoY9AFdCkAR06m6AnThkgZDk6lEoYL9Lol6Hf7ugpv5wI9LTVwHYs2oo2i9FNjgPmgURvlmCQoB+ZHIf5vn8KCKIwhO1kAVr30B+1wb+SPvfg4afuyAdsqG4f3XLKgMi37b3MUIhrk2/+WlE9QxAiNfNT0Tch/irASThb9tFUetl3j1rk2pJjgCBRCAc+9uoQH50WRLW6uyCqAmyy6Av8mTwpglrArfbzCfIoYgtP5FWxu15p5ikY8/vrTm52TqwIlwGtI+EqrgtLUsI4TU97daArLHYKJZUvRekp8ZdTna9J9RX7AWPAmhPe1V/3I3hXrRTfM/JKbTgKpMmJhg0d/K4t/VsOjCTEl/H8DBSn8Mgg2ps21PoEnC4HXZzinjbme6zellbKeYeRxGoovoUEmpMy4tSHm+0UdUc+IakD6++jWMNypB8gAaTW15VV0JDa3Ihz9TksVJrPwckoWUS9IsuEpZXi/1VTncuafWFuZab+4a7fTCr52AavkXn3gh6nWm8qeDzXZGqzJbym08jBJ7S9guzUVGa/uTQ2EuMUCfUpjd84O1QLPQnYuI9uBw6wS0qf536g2q0u8XHs0xI3qgulNFTkHevvslhHwK8pqE6S9vXATRCrMisZ1GC+UpIFSOcY7FsswgIXYk5Ab0e9I7qyXaTES/7G+4u/1KF9uCGwdkmWm/f61o5TDC+BKZcWz/YaXVeHxiXVeH2ii1xvb7miLVMn6y6p0OH5Us1wfYq1gHbJzY6e0U8whjnDOj+a3lxwxJme7q7Q7mzQYWzDyLgy/MPY0pXzoczsoOowunti1o9bED90csLXz7luLNE76r1oE6RL/TzP70cjpUSkydSbuBPK+PFZ+h+0GrKIVfmlJ00VOcwprSpSWlv7bW3KNsmGwnulJb8ZXtKutwOQsZNPS/40K4Wz1yVDlSPG1x00L3fCdme/rzYnELa12wxvK/4FbgWBlabWWNJWsdMmm5fWDoC1M4PKfy6257GYjIQxMUVauYs5l+gpgWPnYsToeQLBWdKkgO5lJuWLt+mvkyIVcPX1nG0f9zNYMHkaJr0eRnScexRf6GefjkrzT67fOkRPqnBkX1EQjA3pCkVCrCvdOwGZ90pf8Yr/uuXz6ghssCSdPdXNqECB0+weqJ5Xv/WeMu7gzT/Ptk/iZ6KK7sWNq5HYSSWrb3ZcSQbgVpGUm3wMgPdmk0fIAs4nAVQT4ra909lKfXn02TS+Y4KSgCzOtC6S0RVmdaE5EeMmFBq66dF8l3jyNub4S3gFDo82EvBOnc6YTXNtGEL+xMB3XvIlF+MGZz8fbBnX0dxSWHIPqQC4yDMqQCwqYMPwsDsKXh5ds9O11BDRF9Bc7HRl2+lse2Hu0OYbUbpJtLFfTvP5UNUugqWJu3vtQxO+UPmfzrJwjagjhWy7X09FjtyAipHMyKFU559y4O+9xP0MrG4lZ3s+NxxLsdHOUjV84QYVMkCa5i1uzoIfq8rag4ZG2KXzRGMGZ475YnUDHTmwx6scRO/eipaHEWXfcQq+ByfEHAYjDp233ZJOE0TBVZxY+rC4Qkr6ccr6WvJoq/a9bweUGSnj6M8V3beUXufDOj/yP2rBljI/rQWi+FTDdSJwjEI30KXvzQLn1+nRrYbOcsLg9ciZGox3YDa4fAjDpGuxhTLG/Yc+13PnNZe/ZVSn+H8y6iuXPXV3VhZmi6SbjwzIs7s7FTkDOToUIHygrenTlo0FPWZZ1o56vlcYgiFAHJsB4GkUFMN5Mj1c7URMv3TAbh6o1CUbfMizwo6aMZk312HW7i6nIV70U2+Ij8QSAJaJMuJtLYE/J+UPEKI7tOSOuNiRXiHGag/63NLTc1nHw6HALrOViIUus2kT8kQKSMSLzqMoTF3PixMi1YRfBxjK5+LCUNLWYiGxq8QV4o7cQtkj6nJofsBmfDfug5jePeV7mf2OkPDrqH6LbgMPwDIvFL1ZAF4rO8NwP26K5zDm5a8P5a1vAwAODOhRCqlteNQHWjfFetdIGQ7zdAAAAAJTMug/5niuZXsXPnhbBReiNsED4ak+EANKft4MAOt4FMtowYrULMoFT7ehtmOvKXnOOXyZN0Y0d+2MMUeu2TDJLFWq3Bpaua3QiX2AgAigA2Tsl3HZt0Tbw0AAAAAAAA) For two backend libraries to be loaded, either a second copy of the library is explicitly loaded from a separate directory than where the first library is located, or a duplicate filesystem is created during process execution (adb remount for android targets). In either case, the two artifact layouts shown above are not supported. If these layouts are detected during runtime, QNN\_COMMON\_ERROR\_LOADING\_BINARIES will be returned from the following APIs: - QnnBackend\_registerOpPackage - QnnBackend\_validateOpConfig - QnnContext\_create - QnnDevice\_create - QnnGraph\_create ## Graph Switching (Beta) Note This feature is currently in experimental beta release, any proposed method of usage and behavior may change in future releases. This feature is used only when client requires further reduction in RAM at the cost of execution speed. The feature currently has limitations as stated below: - This feature does not support concurrent graph execution between the switching graphs. - This feature can not be used together with QNN\_HTP\_CONTEXT\_CONFIG\_OPTION\_SHARE\_RESOURCES config. - Memory is reduced at the cost of slower first execution after graph switching. This feature is a way to lazy load models when needing to execute. This allows multiple graphs to be enabled but in unloaded state, and keeping only one graph(ie. graph1) fully loaded and ready to execute in a *low memory mode* to reduce sustained memory usage. When an unloaded graph(ie. graph2) needs to execute, graph switching (lazy unload graph 1 and load graph2) will take place automatically if this graph switching feature is enabled. The amount of memory saved roughly depends on the total size of the models that are in unloaded state. - To enable this feature in code: (Beta) - 1QnnContext_Config_t* isPersistentBinaryConfig = new QnnContext_Config_t; 2isPersistentBinaryConfig->option = QnnContext_ConfigOption_t::QNN_CONTEXT_CONFIG_PERSISTENT_BINARY; 3isPersistentBinaryConfig->isPersistentBinary = 1; 4 5QnnContext_Config_t* memoryLimitHintConfig = new QnnContext_Config_t; 6memoryLimitHintConfig->option = QnnContext_ConfigOption_t::QNN_CONTEXT_CONFIG_MEMORY_LIMIT_HINT; 7memoryLimitHintConfig->memoryLimitHintConfig = 1; //any non-zero value 8 9QnnContext_Config_t* cfgs[] = {isPersistentBinaryConfig, memoryLimitHintConfig, NULL}; 10QnnContext_createFromBinary(..., cfgs, ..., &contextHandle, ...); Copy to clipboard - Example in backend extension config: (Beta) - { "backend_extensions" :{...}, "context_configs" : { "is_persistent_binary" : boolean_value, "spill_fill_buffer" : int64_value, "weights_buffer" : int64_value, "memory_limit_hint" : uint64_value, "enable_graphs" : ["", "", ...] } "graph_configs" : [{...}] } Copy to clipboard - **is\_persistent\_binary**: default false; to use graph switching feature, is\_persistent\_binary is required. - **spill\_fill\_buffer**: This field sets the spill fill weights to be shared in a buffer shared between the client and backend. The default is 0. > > > - A value greater than 0 allocates the exact spill fill size given by the user. > - A value of 0 disables this feature. > - A value of -1 allocates the spill fill size given by QNN. - **weights\_buffer**: This field sets the weights to be shared in a buffer shared between the client and backend. The default is 0. > > > - A value greater than 0 allocates the exact buffer size given by the user. > - A value of 0 disables this feature. > - A value of -1 allocates the weight size given by QNN. - **memory\_limit\_hint**: default 0, set to any non zero value to enter *low memory mode* with graph\_switching enabled. - **enable\_graphs (optional)**: the name of the graphs that will be enabled. When graph switching feature is enabled, only the first graph in the enable\_graphs list is loaded. When this field is left out during graph switching mode, it signals all graphs in the serialized binary are enabled, and only the first graph in the serialized binary is loaded. User can strategically specify the order of graphs in this enable\_graphs field to control which graph to load first. Note - When is\_persistent\_binary is enabled, it is advised for the client to use mmap to map the binary file passed to the QnnContext\_createFromBinary API call. Once the API call is finished, it is also advised that the client should use techniques such as madvise() to free up some memory held by mmap. Otherwise, the client may experience a high sustained memory due to holding onto the persistent binary. For the same reason, a non-mmaped implementation strategy is not recommended. - Client is responsible for keeping this mmapped buffer alive in the lifetime of context so graph reloading could happen. Client must not unmap the fd until QnnContext\_free is invoked. Freeing/ummaping the buffer prematurelly will result in undefined behavior. - Client is also responsible for freeing up the mmapped buffer when destroying context, other wise may introduce memory leak. - For the memory\_limit\_hint, any non-zero value will enable graph switching. Values greater than zero, will only indicate the low memory mode. Any specific memory\_limit\_hint value will not affect the graph switching behaviour. - Client is responsible for creating and freeing buffer used to store spill fill buffer and weights buffer. ## Multi-Graph Switching (Beta) The Multi-Graph Switching feature enhances the existing graph switching, allowing multiple graphs to be loaded and retained in memory at once. It improves the execution speed compared to traditional graph switching, which unloads the current graph before loading a new one. This feature balances memory usage and execution efficiency. To enable Multi-Graph Switching, users must configure a “graphs\_retention\_order” which is an ordered list of graph names that defines their retention priority. Along with this, users must set memory\_limit\_hint > 0 and is\_persistent\_binary = true similar to traditional graph switching. - Example in backend extension config: (Beta) - { "backend_extensions" :{...}, "context_configs" : { "is_persistent_binary" : boolean_value, "memory_limit_hint" : uint64_value, "enable_graphs" : ["", "", ...], "graph_retention_order": ["", "", ...] } "graph_configs" : [{...}] } Copy to clipboard - **graph\_retention\_order**: This field defines an ordered list of graph names to be retained in memory. Graphs earlier in the list have higher retention priority. When the Multi-Graph Switching feature is enabled, it will preload all of the graphs in this list, provided they all fit into the current PD’s virtual address space. Any other graph that is not part of this list will be lazy loaded at execution. Additionally, if the memory is limited, then the retained graphs may get unloaded, starting with the least important or graphs lower in the retention list. If the graph\_retention\_order is configured without memory\_limit\_hint, then the retention feature will be disabled. If other graph switching configurations are set and this field is left empty, traditional graph switching will be triggered. - **is\_persistent\_binary**: This field is required to be set to true for using the Multi-Graph Switching feature. The default is false. - **memory\_limit\_hint**: This field can be set to any non-zero value to enter low memory mode to enable multi-graph switching. The default is 0. - **enable\_graphs (optional)**: This field sets the name of the graphs that will be enabled. In case of Multi-graph switching, the graph names in the retention list should be in the enabled state as these are loaded during deserialization. When this field is left out during graph switching mode, it signals all graphs in the serialized binary as enabled but proceeds with loading graphs that are part of the retention list. **Example: Multi-Graph Switching with Retention Order** Assume a binary contains four graphs: A, B, C, and D - each of equal size. graph\_retention\_order: [“A”, “B”], memory\_limit\_hint > 0 and is\_persistent\_binary = true. Additionally, consider only two graphs can fit at a time in the PD limit. **Case 1: Execution order A, B, A, B** In multi-graph switching, all graphs from the retention list are loaded during deserialization as long as they fit into the memory. Here graph A and B can both fit, hence are loaded and ready to execute. 1. When graph A comes for execution, it is already loaded and ready to execute. 2. When graph B comes for execution, it is already loaded and ready to execute. 3. When graph A comes for execution again, it is already loaded and ready to execute. 4. When graph B comes for execution again, it is already loaded and ready to execute. With Multi-Graph Switching, more graphs can remain loaded in memory. Here graphs A and B are retained in memory thereby, reducing the reload overhead compared to traditional graph switching. **Case 2: Execution order A, B, C, D** In multi-graph switching, all graphs from the retention list are loaded during deserialization as long as they fit into the memory. Here graph A and B can both fit, hence are loaded and ready to execute. 1. When graph A comes for execution, it is already loaded and ready to execute. 2. When graph B comes for execution, it is already loaded and ready to execute. 3. When graph C comes for execution, it is loaded with graph switching. It will check if the existing graphs A and B along with graph C can fit into the memory. It will also check if any non-retained graphs can be removed. Currently no other non-retained graph is loaded and given our assumption, all three graphs cannot fit into memory. It will unload the least important retained graph B. If graph A and C can fit, C is loaded and executed. Otherwise it will continue unloading graph A for graph C to fit. Here A and C can fit hence C is loaded and executed. 4. When graph D comes for execution, it is loaded with graph switching. Graph C is unloaded as this is not part of the retention list. If graph A and D can fit, D is loaded and executed. Otherwise it will continue unloading graph A for graph D to fit. Here both A and D can fit, hence D is loaded and executed. The execution order in this example demonstrates that retained graphs may be unloaded if memory pressure occurs. Thus the number of graphs retained depends on number of graphs retained, graph sizes, graph execution order and underlying PD limit. **Case 3: Execution order A, B, C, A, B, D** In multi-graph switching, all graphs from the retention list are loaded during deserialization as long as they fit into the memory. Here graph A and B can both fit, hence are loaded and ready to execute. 1. When graph A comes for execution, it is already loaded and ready to execute. 2. When graph B comes for execution, it is already loaded and ready to execute. 3. When graph C comes for execution, it is loaded with graph switching. It will check if the existing graphs A and B along with graph C can fit into the memory. It will also check if any non-retained graphs can be removed. Currently no other non-retained graph is loaded and given our assumption, all three graphs cannot fit into memory. It will unload the least important retained graph B. If graph A and C can fit, C is loaded and executed. Otherwise it will continue unloading graph A for graph C to fit. Here A and C can fit hence C is loaded and executed. 4. When graph A comes for execution, it is already loaded and ready to execute. The non-retained graph C remains loaded until next graph switching is triggered. At the time of the next graph switching, graph C is a candidate to be unloaded as it is not part of the retention list. 5. When graph B comes for execution, it is loaded with graph switching. It will also be marked for retention. However, if at any point, memory pressure occurs, it may be unloaded as seen in step 3. 6. When graph D comes for execution, it is loaded with graph switching. Graph C is unloaded as this is not part of the retention list. If graph A and D can fit, D is loaded and executed. Otherwise it will continue unloading the least important retained graph A for graph D to fit. Here both A and D can fit, hence D is loaded and executed. The execution order in this example demonstrates that any retained graph may get unloaded and require reloading with switching. Additionally, any graph that is not part of the retention list gets unloaded when multi-graph switching occurs. It is important to note that graph\_retention\_order is used to preload graphs during deserialization. Users must ensure that all graphs listed in the graph\_retention\_order list fits within the memory. In the above examples, if the list included more retention graphs than the memory allowed (e.g., [“A”, “B”, “C”] when only two can fit), the configuration needs to be adjusted to ensure provided graphs can be loaded all at once. Note This feature is currently in experimental beta release, and currently has following limitations/constraints: - Graphs from the graph\_retention\_order list are preloaded based on the PD’s virtual address space limit. Users need to configure this list to ensure all graphs can be preloaded. If all graphs from this list cannot be deserialized on any of the PDs then this list needs to be reconfigured taking into account the graph’s memory usage. - The number of retained graphs is highly dependent on the number of graphs in the retention order, execution order, PD limit, and the actual memory usage of the graphs. It may not retain all the graphs from the provided list. - At any point, memory pressures may cause any of the retained graphs to be unloaded. - During multi-graph switching, graphs not in the retention list and not currently executing will be unloaded during switching. - If the graph retention order is empty, by default traditional graph switching will be triggered. - Compared to traditional graph switching, the RAM usage will be higher because more graphs stay loaded. - For the memory\_limit\_hint, any non-zero value will enable graph switching. Values greater than zero will only indicate the low memory mode. Any specific memory\_limit\_hint value will not affect the graph switching behaviour. - Multi-graph switching does not support concurrent graph execution between the switching graphs. - Multi-graph switching cannot be used together with the QNN\_HTP\_CONTEXT\_CONFIG\_OPTION\_SHARE\_RESOURCES configuration. ## Benefits of batch inference and multi-threaded inference Multi-threading hides the cost of CPU/HTP communication time (RPC). In a single threaded inference the utilization of HTP hardware is not efficient as shown in the example below. ![../../_static/resources/multi_thread_and_multi_batch/single_vs_multi_thread.jpg](data:image/jpeg;base64,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) Compare this to the multi-threaded inference. Here, the time used by RPC of the first inference is masked by the inference in the second thread. Thus, HTP hardware spends less idle time. Similarly, using multiple inferences per batch improves the reuse of weights memory, thus avoiding reloading the weights. It is to be noted here that the Activation size is a factor which affects the benefit of using batches. - With low resolution models, weights and activations fit in VTCM. In this case, using batched activations reuses the weights for all the activations, thus increasing the efficiency per batch. - With large resolution models, as the activations take up more VTCM space, therefore weights are reused for lesser number of activations. This reduces the reuse efficiency of weights per batch. Example: With SNPE 2.9, SM8550 (Kailua.LA.1.1-00190 GKI.HY11-1) shows approximately 1.8x benefit for resnet50 when using snpe-parallel-run. - Baseline: Using 1 batch and 1 thread achieves approximately 1500 inf/s - Batching and multithreading: Using batches of 5 and 2 threads achieves approximately 2800 inf/s ## Hexagon NPU Runtime Driver (Windows Only) Hexagon NPU Runtime Driver (HNRD) is available for Windows based Snapdragon® X Series Platforms. HNRD is designed to be forward and backward compatible with Qualcomm AI Stack SDKs. With HNRD in the system, applications have the option to unbundle from QNN HTP platform dependent libraries, and allows these applications to be portable over to older and newer Windows platforms. HNRD is packaged and distributed independently from the QNN SDK and it is currently packaged with the device BSP. OEMs use the BSP to pre-install HNRD on their devices. **Switching Between Traditional and HNRD Paths** ![../../_static/resources/hexagon_npu_runtime_driver.jpg](data:image/jpeg;base64,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) - Pre-driver: - - Traditional path only; applications build with the QNN SDK and bundle the QNN HTP platform dependent libraries - Post-driver: - - Traditional and HNRD paths available; the application can choose which path to use - Traditional path; applications build with the QNN SDK and bundle the QNN HTP platform dependent libraries with the application (Default – same as pre-driver) - HNRD path; applications build with the QNN SDK, but utilize the platform dependent libraries installed on the system - Note: there is no difference in the build step between traditional and HNRD paths. In addition, the bundling and choosing between traditional and HNRD paths is applicable to both QNN and SNPE In other words, if an application bundles the QNN HTP platform dependent libraries (i.e., QnnHtpV73Stub.dll and QnnHtpV73Skel.so), it will default to choose the traditional path. Otherwise, if the platform dependent libraries are not bundled, it will fall back to choose the HNRD path. The following sample logs illustrate the HNRD path being applied: 1 0.0ms [WARNING] QnnDsp Traditional path not available. Switching to user driver path 2 0.0ms [WARNING] QnnDsp HTP user driver is loaded. Switched to user driver path Copy to clipboard **Compatibility Support** The minimum QNN and SNPE version is 2.22.2. Applications can be built with older or newer versions of the QNN SDK and they will still work on the device. Depending on the HNRD version installed on the system, new features may not be supported. Note that traditional and HNRD paths can co-exist on the platform and each application independently selects whether to use traditional or HNRD paths. **Context Binary Management** When utilizing [Context caching](https://docs.qualcomm.com/doc/80-63442-50/topic/api_overview.html#context-caching) with HNRD, it requires special attention on managing the context binaries. When using HNRD path, online prepare and context binary loading are done by HNRD since they are platform-dependent. A context binary generated by one version of HNRD might not be able to run on HNRD with an older version or might not utilize all software / hardware capabilities on HNRD with a new version. It is required to check the compatibility of saved context binaries with the HNRD every time before loading, since the HNRD installed on a device can be upgraded (or downgraded) at any time. [QnnContext\_createFromBinary()](https://docs.qualcomm.com/doc/80-63442-50/topic/function_QnnContext_8h_1aa261465dd9bdfe51532a9d33fe4a2fa9.html#exhale-function-qnncontext-8h-1aa261465dd9bdfe51532a9d33fe4a2fa9) checks the compatibility automatically before loading the context binaries. Setting [QnnContext\_BinaryCompatibilityType\_t](https://docs.qualcomm.com/doc/80-63442-50/topic/enum_QnnContext_8h_1aea32d8657e3ab422aebf55f3adef7670.html#exhale-enum-qnncontext-8h-1aea32d8657e3ab422aebf55f3adef7670) can control whether to fail sub-optimal context binaries during compatibility check. [QnnContext\_validateBinary](https://docs.qualcomm.com/doc/80-63442-50/topic/function_QnnContext_8h_1af04e61e7ab7468913f7906a8f9c7123b.html#exhale-function-qnncontext-8h-1af04e61e7ab7468913f7906a8f9c7123b) does a similar check as [QnnContext\_createFromBinary()](https://docs.qualcomm.com/doc/80-63442-50/topic/function_QnnContext_8h_1aa261465dd9bdfe51532a9d33fe4a2fa9.html#exhale-function-qnncontext-8h-1aa261465dd9bdfe51532a9d33fe4a2fa9) except it won’t create the context. If a context binary fails to pass [QnnContext\_createFromBinary()](https://docs.qualcomm.com/doc/80-63442-50/topic/function_QnnContext_8h_1aa261465dd9bdfe51532a9d33fe4a2fa9.html#exhale-function-qnncontext-8h-1aa261465dd9bdfe51532a9d33fe4a2fa9) or [QnnContext\_validateBinary](https://docs.qualcomm.com/doc/80-63442-50/topic/function_QnnContext_8h_1af04e61e7ab7468913f7906a8f9c7123b.html#exhale-function-qnncontext-8h-1af04e61e7ab7468913f7906a8f9c7123b), performing online prepare to create another valid context binary can solve the problem. To reduce the latency impact of online prepare, one can continue execution with the original sub-optimal context binary while doing online prepare in a background thread and switch to the new context binary once online prepare is done. Or one can first do a fast prepare (e.g., by setting QNN\_HTP\_GRAPH\_OPTIMIZATION\_TYPE\_FINALIZE\_OPTIMIZATION\_FLAG to 1) to execute with a sub-optimal context, and then do a slow prepare (e.g., by setting QNN\_HTP\_GRAPH\_OPTIMIZATION\_TYPE\_FINALIZE\_OPTIMIZATION\_FLAG to 3) in a background thread. Note that online prepare may not succeed. This can happen when a model converted by a newer SDK version uses features that are not supported by the HNRD. In such a case, upgrading the HNRD. The following example shows how to handle the context binary management. 1Qnn_ContextHandle_t context; // The context used to do inference 2std::future futureContext; // The context being created in background 3 4// Create from binary and set compatibility mode to strict to check sub-optimality. 5Qnn_ErrorHandle_t result = 6 doCreateFromBinary(QNN_CONTEXT_BINARY_COMPATIBILITY_STRICT, &context); 7 8if (result != QNN_SUCCESS) { 9 if (result == QNN_CONTEXT_ERROR_BINARY_SUBOPTIMAL) { 10 // The context binary is valid but sub-optimal. 11 // Continue execution with this context binary. 12 // Set compatibility mode to permissive to bypass sub-optimality check. 13 doCreateFromBinary(QNN_CONTEXT_BINARY_COMPATIBILITY_PERMISSIVE, &context); 14 } else if (result = QNN_CONTEXT_ERROR_CREATE_FROM_BINARY) { 15 // The context binary cannot run. 16 // Do fast prepare with optimization level 1 without saving the context binary to file. 17 context = doOnlinePrepare(HTP_OPTIMIZATION_LEVEL_1, NO_SAVE_CONTEXT); 18 19 // Here assumes nullptr will be returned if online prepare fails. 20 if (context == nullptr) { 21 // If online prepare fails, one possible reason could be HNRD is too old for the 22 // graph. In such case, prompt the users to upgrade HNRD. 23 message(ERROR, "The HNRD is too old. Please install latest HNRD."); 24 } 25 } else { 26 throw std::runtime_error("Skip handling of other errors."); 27 } 28 29 // Now we have a sub-optimal context in variable "context". 30 // Do online prepare in another thread to produce the optimal context and save it. 31 futureContext = std::async(std::launch::async, doOnlinePrepare, HTP_OPTIMIZATION_LEVEL_3, 32 SAVE_CONTEXT); 33} 34 35// Do inference. 36while (waitInputData()) { 37 // Switch to optimal context if prepare is done. 38 if (futureContext.valid() && 39 futureContext.wait_for(std::chrono::seconds(0)) == std::future_status::ready) { 40 context = futureContext.get(); 41 } 42 43 doInference(context); 44} 45 46// Clean up. 47doFreeContext(context); Copy to clipboard ## QnnContext\_createFromBinaryListAsync API This API provides a method for asynchronously initializing multiple context. Currently only supported on Mobile and Windows platforms. binaries (models) in a single API call. **It offers two primary features:** - Initialization Time Optimization: Optimizes the time taken to initialize the context binaries. - Resource Sharing: Optimizes runtime memory usage and/or HTP virtual address space based on the HTP custom context configurations [QNN\_HTP\_CONTEXT\_CONFIG\_OPTION\_SHARE\_RESOURCES](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#share-resource-configure-demo) and [QNN\_HTP\_CONTEXT\_CONFIG\_OPTION\_SHARE\_RESOURCES\_OPTIMIZATION\_TYPE](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#share-resource-configure-demo) **Notifications and Handles:** - For each graph within a context, and for all contexts, a notification will be sent after they are initialized along with the initialization status. - These notifications may arrive before or after the API returns. - The order of the notification depends on the initialization time of each context and is not guaranteed to follow any sequence. - For a context with multiple graphs, there will be a notification for each graph and an additional one for the context. - Valid graph and context handles will be sent back to the client through these notifications. Clients can then freely use these handles. **Guidelines and Limitations of using this API** - It is highly advisable to use a single thread to call this API and no other QNN API should be called in parallel. > > > - Multiple application threads trying to initialize multiple use cases in parallel > is not fully supported and can result in Input/Output Memory allocation or mapping > failures during graph execution or during `QnnMem_register()` call. > - We internally parallelize model initialization (the driver decides at runtime whether > to do it on CPU, HTP, or both), fully utilizing the backend to minimize the > initialization time. Therefore, calling any other QNN API in parallel can result > in over-subscription and degrade performance, which is counterproductive. - Clients should not use the context configuration option `QNN_HTP_CONTEXT_CONFIG_OPTION_REGISTER_MULTI_CONTEXTS` or `QNN_HTP_CONTEXT_CONFIG_OPTION_REGISTER_CONCURRENT_RESOURCE_SHARING` to register multiple contexts, as they are explicitly designed for the `QnnContext_createFromBinary()` API. - When the HTP custom context configuration ([QNN\_HTP\_CONTEXT\_CONFIG\_OPTION\_SHARE\_RESOURCES](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#share-resource-configure-demo)) is enabled, it is recommended that clients use I/O buffers that utilize the Multi-Tensor shared buffer. This method maps a group of tensors to a single shared buffer, optimizing both space and initialization time. Additionally, when using this option, the batch size of the graph should be set to 1 to prevent reallocation of the shared buffer. **Usage Diagram:** Refer to the following diagram for a visual representation of the API usage. ![Context Create From Binary List Async Callflow Diagram](data:image/png;base64,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) ## Multiple cDSP Sessions Note - This feature is often referred to as Multi-PD, a term that can be misleading as *PD* (process domain) technically denotes a different transport session. - Please refer to Hexagon SDK Documentation for more information. - Certain Snapdragon SoCs are capable of creating and opening multiple sessions within cDSP from a single CPU application. The number of concurrent sessions that can be supported per CPU application process, as well as the total number of sessions that can be supported within cDSP, are dependent on the hardware configuration of the SoC. - cDSP is a 32-bit processor, and each session supports a virtual address space of 4 GB; however, only 3.75 GB of this space is usable. - The maximum amount of memory per CPU process is constrained by either the total free RAM available, or the product of the number of sessions supported per CPU process and 3.75 GB, depending on which of the two is lesser. **Model Loading Across Multiple Sessions** Here are some key aspects: - When loading the offline-prepared model (via `QnnContext_createFromBinary()`, `QnnContext_createFromBinaryWithSignal()`, or `QnnContext_createFromBinaryListAsync()`), the backend will attempt to deserialize the model on the first available session. If the attempt fails due to memory availability, the backend will attempt to deserialize the model on the next available session. - The initialization time for a model, which will be deserialized on sessions other than the first one, will be slightly higher. This is because the backend will try to load the model on all of the available sessions sequentially. - A single context binary cannot be split across multiple sessions. Therefore, it is expected that the client will split the model before running QNN converter tools. Optimum split points are decided based on the network topology, precision used (a8w4, a16w4, a8w8, a16w8, etc.), and the maximum virtual address space per session. - Although the maximum usable virtual address space available per session is 3.75 GB, it is recommended that clients limit the heap required for a single context binary to be under 3 GB. - The decision for which session to use for a context binary is determined by the backend. There is no API to specify the session while loading a context. The sessions used are transparent to the client application. The following example will help understand the workings of multi-sessions. The algorithm may vary slightly based on the API used to initialize the models; however, the backend will generally try to efficiently pack the models on a single session. - Consider an ideal scenario with no fragmentation and the memory required for the model deserialization is exactly equal to the context binary size. Assume we have five (5) models with sizes 1.5 GB, 1.5 GB, 1 GB, 1 GB, and 250 MB respectively, with the models loaded sequentially in this order. - The first model’s deserialization attempt will happen on session #0. Assuming session #0 has nothing loaded right now, it will get deserialized successfully. - The second model will also get deserialized on session #0 as there is still space available on it. At this point, session #0 now has 3 GB of virtual space occupied. - The third model’s deserialization attempt will happen on session #0 first. This will be unsuccessful as there is not enough space available on it. Then backend will create a new sesion (say session #1). Session #1 has 3.75 GB available and hence the third model will be deserialized successfully on it. - The fourth model’s deserialization attempt will be made on session #0 first. This will be unsuccessful for the same reason as the third model. The next deserialization attempt will be made on session #1. This will be successful as only 1 GB was occupied on session #1. - The fifth model’s deserialization attempt will be made on session #0. As the model requires 250 MB and we have that much space available on session #0, the model will be successfully deserialized on it. Please refer to the diagram below for an illustration of this example: ![Multi PD illustration](data:image/png;base64,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) **Guidelines and Limitations for I/O Buffer Allocation/Mapping Failures:** As described earlier, QNN HTP runtime uses an *Efficient Packing* mechanism to deserialize context binaries on different sessions. This means that if a context binary can fit on the current session, then the backend will deserialize the context binary on that session. If it does not fit, then it will try on the next session. Typically in use-cases where shared buffers are used for inputs and outputs (I/O), clients register externally allocated memory with the backend through the `QnnMem_register` API (avoids time spent on memory copy). When the models are large (LLMs/LVMs), their I/O also tend to be larger. In those cases, clients might encounter memory registration failures. This happens due to the virtual address space being occupied enough that there is no space available for the I/O to be mapped. Although this is predominant in shared buffer use cases, it can happen without those as well. Note that the I/O buffers specific to a context need to be mapped to the same session where that context is deserialized. Consider the following example: - There are five (5) context binaries out of which the first four (4) context binaries can fit into the first session (session #0), and for the fifth context binary, the backend spawns a new session (say session #1) and deserializes it there. - After deserializing all the context binaries, the client application is trying to register I/O buffers for each of the contexts and one of the mappings fails due to unavailability of the space on session #0. - If context 4 would not have been deserialized on session #0, and provided the fifth context is on session #2, session #0 would have been less packed leaving space for those buffers. Please refer to the diagram below for an illustration of this scenario: ![I/O Buffer Registration Failure](data:image/png;base64,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) Recommendations to address the issue: - Use the custom context configuration ([QNN\_HTP\_CONTEXT\_CONFIG\_OPTION\_IO\_MEM\_ESTIMATION](https://docs.qualcomm.com/doc/80-63442-50/topic/htp_backend.html#io-mem-estimation-usage)) to make sure the space is available on the session for I/O buffers. This does have a limitation that models can only be initialized sequentially, and right after the model is initialized, the I/O buffers for that context should be registered. This also increases peak RAM during model initialization; however, sustained RAM usage would be identical to the case where this option is disabled. - Another potential way to alleviate the issue is to introduce a dummy memory registration call. If a client runs into a memory registration failure issue, they can register a dummy buffer that can force the context to deserialize on to the next session, thereby bypassing the issue. Clients would be expected to free the dummy registered memory after the initialization of all contexts is completed. - Use the `QnnContext_createFromBinaryListAsync` API to initialize the large model use cases. This API can be enabled with virtual address space optimization with which the backend internally takes care of making sure enough space is available for I/O to be mapped later. So after all models are initialized, which can be done through a single API call, I/O can be safely mapped to the PD. ## Init Cancellation The QnnContext\_createFromBinaryWithSignal API allows users to interrupt the context creation process using two types of signals: abort or timeout. Abort Signal: This signal enables users to interrupt the API based on their requirement. Timeout Signal: This signal will automatically interrupt the API after a predefined timeout period. For more details regarding signal config refer [QnnSignal.h](https://docs.qualcomm.com/doc/80-63442-50/topic/api-rst_file_include_QNN_QnnSignal_h.html##file-include-qnn-qnnsignal-h) ![../../_static/resources/qnn_htp_init_cancellation_timeout.png](data:image/png;base64,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) ![../../_static/resources/qnn_htp_init_cancellation_abort.png](data:image/png;base64,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) ## Graph Priority Setting **Graph Priority** involves assigning priorities to both **HMX** and **HVX** threads during graph processing. **Priority Behavior** When a priority level X is specified: (Assuming Y is the corresponding HTP Thread Priority, refer below table for mapping) > > > - **HMX thread(s)** are assigned priority Y > - **HVX thread(s)** are assigned priority Y + 1. **NOTE** : A higher numeric value indicates a **lower** execution priority. **QNN to HTP Thread Priority Mapping** The table below outlines how QNN priority levels map to HTP thread priorities: HTP Thread Priority Derivation | QNN Priority Level | HMX Thread(s) Priority | HVX Thread(s) Priority | | --- | --- | --- | | QNN_PRIORITY_LOW (0) | 0xC5 | 0xC6 | | QNN_PRIORITY_NORMAL / QNN_PRIORITY_DEFAULT (100) | 0xC0 | 0xC1 | | QNN_PRIORITY_NORMAL_HIGH (150) | 0xBD | 0xBE | | QNN_PRIORITY_HIGH (200) | 0xBB | 0xBC | For more details on priority levels, refer to the **Hexagon SDK documentation**. ## Setting Graph Priority HTP backend supports setting graph priorities by given parameter [Struct QnnGraph\_Config\_t](https://docs.qualcomm.com/doc/80-63442-50/topic/structQnnGraph__Config__t.html#exhale-struct-structqnngraph-config-t) with `QNN_GRAPH_CONFIG_OPTION_PRIORITY` when calling [QnnGraph\_create](https://docs.qualcomm.com/doc/80-63442-50/topic/function_QnnGraph_8h_1acef7b5c50463d8b02bb4abd769ae5ea9.html#exhale-function-qnngraph-8h-1acef7b5c50463d8b02bb4abd769ae5ea9). The graph priority is set to `QNN_PRIORITY_DEFAULT` by default if no configuration options are provided. Clients may also modify the priority of an existing graph using [QnnGraph\_setConfig](https://docs.qualcomm.com/doc/80-63442-50/topic/function_QnnGraph_8h_1ac0ba7ace966622bc3967a29f334b157e.html#exhale-function-qnngraph-8h-1ac0ba7ace966622bc3967a29f334b157e). The usage of `QNN_PRIORITY_NORMAL_HIGH` and `QNN_PRIORITY_HIGH` is restricted from general developers. HTP backend allows clients to modify graph priorities using [QnnContext\_setConfig](https://docs.qualcomm.com/doc/80-63442-50/topic/function_QnnContext_8h_1a380694614a9167136c744b21da34feb7.html#exhale-function-qnncontext-8h-1a380694614a9167136c744b21da34feb7) by passing [Struct QnnContext\_Config\_t](https://docs.qualcomm.com/doc/80-63442-50/topic/structQnnContext__Config__t.html#exhale-struct-structqnncontext-config-t) with `QNN_CONTEXT_CONFIG_OPTION_PRIORITY`. When calling this all graph priorities in this context are updated. ## LLM native KVcache The native KVcache feature is designed to optimize the LLMs performance executed on HTP backend. HTP backend transforms the input KV tensors to HMX format for HMX operations. This transformation process is expensive, and the overhead increases with longer context lengths. Native KVcache avoids this costly transformation by keeping Input KV tensors in HMX layout. This feature is typically integrated into an LLM pipeline, where the KV management module is responsible for updating the KVcache in the HMX layout format. **Benefits** - Improved TTFT and Token Rate, especially for large context lengths - Reduced Power Consumption, particularly beneficial for large context lengths. **Constraints** - Applicability to only GenAI LLM Models with KVcache mode - The KVcache tensors must be uint8 and symmetrically quantized - KVcache must be either fully in native format or not at all. Partial use, where some decoder layers use native KVcache while others do not, is not supported. - Context lengths should be a multiple of 256, attention head\_dim should be a multiple of 64 - The operator for concatenating new and old KV in attention must be ScatterElement instead of Concat, the supported single-head structures are illustrated in below graph. - For ARN where N is multiple of 32, the KV input must be in native format. The KV output can be either native or flat. Using native KV output can accelerate kvupdate during TTFT, compared to using flat KV output. - For ARN where N is not multiple of 32, AR1/4/8/16 are typically used. Please ensure 1) use native KV input 2) use flat KV output 3) scatter\_index <= (context length - roundup(ARN,32)). In this case, the client is responsible for the conversion of flat-output to native-input ![../../_static/resources/nativekv_graph_pattern.png](data:image/png;base64,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**How to enable** To enable and use native KVcache, please ensure the input model meets all the constraints. Users can refer to the QC notebook to export a model that supports native KVcache. Then users need to specify the dataFormat for KV cache I/O tensors as QNN\_TENSOR\_DATA\_FORMAT\_HMX\_WEIGHT\_LAYOUT during qnn-context-binary-generator, use `--data_format_config` argument and give path to JSON file $ qnn-context-binary-generator --data_format_config DataFormatFile.json \ --backend [SDK_PATH]/lib/x86_64-linux-clang/libQnnHtp.so \ --dlc_path [MODEL].dlc \ --model libQnnModelDlc.so \ --output_dir [OUTPUT_DIR] \ --binary_file [CONTEXT_BIN] \ --config_file HtpConfigFile.json Copy to clipboard A sample of DataFormatFile.json to enable both native KV input and native KV output. All KV tensors must be included. { "graphs": [ { "graph_name": [...], "tensors": [ { "tensor_name": "past_key_0_in", "dataFormat": "QNN_TENSOR_DATA_FORMAT_HMX_WEIGHT_LAYOUT" }, ... { "tensor_name": "past_key_0_out", "dataFormat": "QNN_TENSOR_DATA_FORMAT_HMX_WEIGHT_LAYOUT" }, ... ] } ] } Copy to clipboard A sample of DataFormatFile.json to enable native KV input only. { "graphs": [ { "graph_name": [...], "tensors": [ { "tensor_name": "past_key_0_in", "dataFormat": "QNN_TENSOR_DATA_FORMAT_HMX_WEIGHT_LAYOUT" }, ... ] } ] } Copy to clipboard Note Currently QNN\_TENSOR\_DATA\_FORMAT\_HMX\_WEIGHT\_LAYOUT is only supported in native KVcache scenario. **HMX\_WEIGHT\_LAYOUT explanation** Assume the flat shape of a single head KV is [DIN, DOUT], where DIN represents the number of input channels and DOUT represents the number of output channels. The transformation from flat to HMX\_WEIGHT layout is [DIN, DOUT] -> DOUT/KV\_TILE\_SIZE \* [DIN, KV\_TILE\_SIZE] -> DOUT/KV\_TILE\_SIZE \* [DIN/32, KV\_TILE\_SIZE/32, 8:DIN, 32:KV\_TILE\_SIZE, 4:DIN] -> [DOUT/KV\_TILE\_SIZE, DIN\*KV\_TILE\_SIZE/1024, 1024] KV\_TILE\_SIZE is fixed in this SDK version. K\_TILE\_SIZE=256, V\_TILE\_SIZE=64 **Other Limitations** - Cannot support v68 targets or lower - The maximum context length that can be supported is limited by the fixed VTCM size. For example, 2M VTCM won’t support 16K context length or above. ## MaskedSoftmax The MaskedSoftmax feature is designed to optimize the LLMs accuracy and performance executed on HTP backend. MaskedSoftmax is used to replace the Softmax(Add(In, Mask)) structure in attention block in LLMs. MaskedSoftmax can use the attention\_mask tensor to mask out the invalid tokens in the softmax operation directly instead of using adding operation. **Benefits** - Improved LLMs accuracy - Improved TTFT and Token Rate, especially when valid tokens account for a small part of large context lengths **Constraints** - Applicability to only uint16 and uint8 quantization for now **How to enable** To enable MaskedSoftmax, model structure must be one of the following three patterns. ![../../_static/resources/MaskedSoftmax_model_structure.png](data:image/png;base64,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) Note - The difference of the above three kinds of structure is the datatype of attention\_mask and how to treat the attention\_mask as the condition of Where op. In pattern A and pattern B, the attention\_mask is quantized to be uint16. In pattern C, the attention\_mask is quantized to be uint8. In pattern A, entries with zero value in the attention\_mask will be kept to perform softmax operation, and entries with non-zero value in the attention\_mask will be masked out. In pattern B and pattern C, entries with zero value in the attention\_mask will be masked out and entries with non-zero value in the attention\_mask will be kept. - The Equal op in pattern A or NotEqual op in pattern B following attention\_mask is used to convert the attention\_mask to a mask tensor with 0 and 1, and the mask tensor is used as the condition of Where op. - The Where op is used to mask out the invalid entries in the input tensor, and the output of Where op is the input tensor of Softmax op. If the condition is false, the corresponding entry in the input tensor will be masked out and MaskedSoftmax will output 0 in the entry. If the condition is true, the corresponding entry in the input tensor will be kept and will be carried out softmax operation. - The Add(ReduceMin(Input), B) structure is used to avoid quantization/dequantization loss. To make sure that the output of softmax in the invalid entries is 0, the parameter B of Add op is currently constrained to be less than or equal to -20.0. ## QnnContext\_createFromBinaryWithCallback API This API provides a callback-based registration mechanism for client-defined buffer allocators and data loading strategies, facilitating synchronous context creation. The client-defined callback is used to override the default buffer allocation and data loading behavior of QnnContext\_createFromBinary, allowing for customized and flexible context binary loading. The Qnn\_ContextBinaryCallback\_t\* callback parameter is extended to support external buffer allocation/deallocation and custom data loading workflows. It enables clients to define how context binary data is provided and released during context creation.This structure primarily includes the following client-defined callback functions: > > > - Qnn\_ContextBinaryDmaDataProviderFn\_t / Qnn\_ContextBinaryRawDataProviderFn\_t > - Qnn\_ContextBinaryDmaDataReleaseFn\_t / Qnn\_ContextBinaryRawDataReleaseFn\_t In each DataProviderFn\_t callback, the client must ensure that the buffer for the requested data section has been properly allocated and the corresponding data has been fully loaded by the time the callback returns. Therefore, both buffer allocation and data loading schemes must be provided together to ensure correct and complete context creation behavior. For detailed definitions, refer to the Qnn\_ContextBinaryCallback\_t structure in QnnContext.h. **Key Features:** > > > - Customized buffer management: Allows clients to manage external buffers and perform customized data loading strategies for context data (e.g., weights). > - Initialization time optimization: When using Qnn\_ContextBinaryDmaBufferCallback\_t, clients can leverage direct I/O scheme to load context binary data directly into external DMA buffers. This avoids runtime copy from user-space to backend buffer and reduces memory and time overhead. Performance gain depends on the client’s callback implementation. Note - In the Qnn\_ContextBinaryCallback\_t definition, two callback types are introduced: - - Qnn\_ContextBinaryRawBufferCallback\_t: For allocating standard raw buffers together with loading corresponding data. These buffers are typically user-space buffers not directly shared with the backend. - Qnn\_ContextBinaryDmaBufferCallback\_t: For allocating DMA buffers together with loading corresponding data. These buffers can be directly shared with the backend, enabling zero-copy context data loading. A typical use case is allocating and loading model weights buffers. Currently, only Qnn\_ContextBinaryDmaBufferCallback\_t is supported. The raw buffer callback type is reserved for future extensibility. The DMA buffer callback is currently only used for model weights allocation and data loading, referred to as the external weights-loaded buffer feature. **Guidelines and limitations for using this API:** > > > - It is highly recommended that the callback implementation uses a direct I/O scheme in combination with Qnn\_ContextBinaryDmaBufferCallback\_t to achieve the zero-copy context binary data loading. > - Proper data alignment support was introduced in the new context binary data format starting from qairt-2.37. Therefore, when using the direct I/O scheme, the context binary must be generated using qairt-2.37 or later. > - This API is extended based on the QnnContext\_createFromBinary API and does not support QnnContext\_createFromBinaryListAsync. As a result, features associated with the ListAsync API—such as QNN\_HTP\_CONTEXT\_CONFIG\_OPTION\_SHARE\_RESOURCES and VA reservation for shared resources—are not supported. > - It is not supported together with the graph switch feature. > - It is not supported together with the udma64 feature, which can be disabled during the graph preparation phase. > - It is not supported together with the QNN\_CONTEXT\_CONFIG\_OPTION\_DEFER\_GRAPH\_INIT. > - It is not supported together with the securePD model protection feature. > - Currently it is only supported and validated on Android platform. Other platforms may require additional adaptation and validation. **High-Level Usage Pipeline:** Refer to the following diagram for a visual representation of the API usage. ![Context Create From Binary With Callback Callflow Diagram](data:image/png;base64,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y7+XqMK6imoGovJkMNUHUa9LjRl24hulfUq775q1jNL44oSkY02GRiQL6TLUR5nCx4tkqACGmDvQ+iD8KylJVyTDiwg3H9hd03La39lFwJt+fE3Sy0y6gkZ8sD7tQiMj54eZbkikESdzo9vYPiJaSgimrjWtWngrZtEeuhbXtywQ/rnt2dXJXGr/cc5fJEtaFf4U4RD+rvDMTu2jpHf1Lr/jBdnD5C8J38a79fF5f9oPuUiDyS4/AhjsPgB7QnXfBQXOZ5+GAILK6HugPl6MuMxxFE0kmISaPfeNqGZ1RStDTLw72iW2WWznJ4O4RDY3oK22i94PeUD3B09QeE6y14q5sIc3qkQdu+C/dMxNoUWqwPFQvUuTnj7QX9CCadJMHl4aIR8mmRvshC/kcVowyHrEK8DUElxSx1IGb5DxqVoLyJlyuQYLsG6nrnGVLvLaXpU/6MlcpArqrioUT66VqmgY4ChJSxjE0m5Qr4GVlYorWTZrSMWvG1tM7HfGBniZqYajA980iVYKizPlLGCbnOZiU9hoilqTdw3icaxl6XFEEVjjctFQuT+ZZmBTvPTs7p5zpzLRQQEdDATam02Gj/lA93A6ZvB/d+Du5rRm/TquDOc6CtqQh4Wlm0509C5hC1RsU/RYd2WLgOiwH1pqJBWOXMSdWN+Ss4w8IrUzN8UVV+6M2DcIhq2PdfWau17Lj5a9z76HnFm25MrVO4/HsXM5EPlzYLE94VfV2ejb4CnRK4luI+I2k4uphl8GO1sjKd1UORdXBGGx3W3B/fqX7KWU4KkOm3RYkjNvVGNjuuzp9kemL0oW+KEARO1L9BH6smAp033Rv0Ykx1luB7k8CGUqGVP6qFSg+kcB8+X4596YmA7G550IgalD1Qaiv5kpGZKj6iWFML3fZg7WsbG43jl+XwuTdT8vOyrOSENAL71TI/3jqAiaUkdRxVsx+rTOUtsnETiR7MFDjHz9esO4Ovmw/Ipocx7AuTMK8afztMECR82KZBDyZ9WdQFSTHW88Lod2ArMEJGVq8ebjjvJ52U0QSLUGkUK6zjLnfEGm0c6qLjoCbCo9+nHQ2ZmnKvOtSagAE6Dvwd089TBl3GdtxooEUWe1sBBSYxcJt+IvAuMWeyEteDP7c+ve5aqsT4L20DZs0F2M2nVXcyUHHTKeFdvDgF28iYWLx4B42Bvc//nh5Uo7m99b8zeUs3NlHpwjQSCiQCZRhzrMUVyaLnkLONo0HZTMFNNKrx5ah/t6EBnAB4W+tP5Oo8QDx08GA1nh589yqywa2UMnNFLpOOpT9EvcfiRweWH0SAOMX9Ett18Apwj6iH0Cl718Z5/+HlxxxK0Iv/7y0PdnxCrX0+Zq5KO6nkoL0AqNuEQENtC1VwrSCX4LuvEKERtONhv7DxkRKu+Uc52lsIatjr96bcjnGaeI5BYjDFRlFweEbosrc90weDAphx3gaiHJSCXhyiUQScd9qDJVLXgiJphwPNlT1j4fB2RPxXj6xPO5m/d6qaOz+BYZkt+wn0IoVJZy1WFm4cjWAOTRBnqpKsSZaUweY0depmPyYLaza1IlH3NbmuaRlGq3O0kYr8Y2N4EIfiVSKFkD5rVV49LmlLtM8PH1rgvlRme7qxOfNGjszOdcs3EB0TS9hzD/nZGo+g2GVHFmpSUSfB7ikB5DEiE9+TzOtMVS/2T5LIrz0RKisz4ATtZ3+kOr8rCSFecIMfOJWHZ/3bkLduJBkUrj/VUQsDC95oPabJVe6x9/5VNn4vtenPbUjAxXcmhrkxmASadhBCE8ONbXmeUfiQP557bu9YHXGu/ZXLGCe+uRr9+BTMA3ep0sxjFyVhWNEne9Jb781qv2mbZfuxeRySVdQr6jcCtzw8T4pMgkZxGOOr4+dGZqn+LRPYjmFaESvh+0YeuO5gExT3+MeWxmoDs3X0BuTpzAgE8VAoIltRbuyq1EKbQiTxHOvcHQrmAnlGu6ZG3unCuQjB9ZiyonThq9TfiRNtYK1S2Fz2Ca4XYreWEg9Q7eC9MTLKKSCZlj6dCuBsxaQY98fDJ43mwuqKLiy0LkC5LDz+dphrrBII6l53bZt5KEike5Sxd21Bx4HKK8iZjx7n366ujH/AzUNkOJNPX8Ha7xCEAYzmHv8Y6U2y+CK9aZFBSPj4r7vhZc403r3qIB6jcxHVJjLkt4QHhNqo9pBXmN0SnT/ezpxa3U9058vKsZgwBjTy0Meu1kyhnosQVv0bTY5W2gspBi2IRHPy2VleQNOV8pLw282NVGbsMbU00uOWYnW2wXtAMshQbNlvO8QX+ha56B/bzdApYatH9i0tm/5qILj7Po0VZt1B522cZ56ngN5DX86qZyAggww4DDU8uBlXiiAI2/wVo38vDcJPT1XlpskV0G+F54+ixbd+Mc19bvO0PqyfICjIxBmOXSY40LPCG63248R/HF/HcnGVKJKLebmq2QxFCbZjNpWPhfiJ/51y/e0PCQgd72LyOSSrqFfUbgVueHifBTB1kBRVavJQtJqd4dmnV+lsHTcuIE2FXfZ4q2lpFrwTZbfqzEMOAGiju3cMVrghMukzrwmz1kD8ItiDcT/S3yoZ0lF7gEZMBcbTSXi0IAAAAAAUcGwCzgc7Fo3Ki+eHZ+x8AhWqjElqMUYoxRF9ncV5TzwTfyKbGEjVb/uclL4k7me4KdbpWENM2UuCUnuniK/ANkNnO8+hDDE8ZHN3VVspalYhTN4O//hADwuUoxBPalu899x7iN7nHK0kf6Lauw7NYSZvnKu9aZuziwSwi8pJK3SXFcAGn73dLPbCl8te0ap3aco0P5lRhoCAb8O6TDFX+gwhg7PpP9uWIQrGds5RIutCMtuf5p0zun8PHjH9oRzNxJqfWqSnG1aJCI8JQGasiJTwTPDF99r9kPcH0qvBKFxVaIrZ2b/E4Al2TZzC+k7RvA1oNea/a0RxgVKlkxGCsmIoefB3HzveCS4+91ZGb2IRYqHTVS6uirS/DVZDW8Gz9lLgXHt2JU0/oJIvcUFT1XmNzOTU+/k7fPiIEM96FRHtXPYWf5pdVKc/FpaYh+uzI7ikz/zlpzGs6aQutwcvWWeJDHENFouPxsgoA1LbCXeHOUryEpV5i9JMgyEZFDoamODv+QmU6m60uHhM6j3Y994cYvI8DLtJ1uxXndqjujXQ/VOYY6qmpAIsgWafH0KwaS7w5yjBZPvGzoJElp+aCf/GQwqKn6k5F9dhoGvzykCbpkP7ntc4QdKSwtSav8YiSWJ669Z+c8a4tOqkWglwowDgYCueCsdoY9mS3pbH8UPDK2ofi4KbHX1uKFCJBFHBaZlHL75m4HIl+efFyA/II5Id4QO3/mo8LytZPYcm1MyxC3uJ6lcbZ5KkFQi2XkFmgXF50Xk1x/Qck2aMK2fqAwHl6Yngxq42/gy+aL0lhgYeaADQJsOr2Mha3YGQ3KWGNuNCs7wuT6vB60wEjFS0k3qcqCRtNKkDZUAgkRTJnfJ9/jgo4Zaz18u5B7paX7nTWLV9PGw6nI/ZTmJlrdZZ0YQqDPtXlUyhz7VigLxTlhzDNkBDjSKrFzlVOeFw3ShLSnfsgJ4moIxpMi/dxMalrVPYbZADWWYtpEnZPdbjBC07GSzKg/MEygSl7WQuuExC9XvzCeSleBo3Jc3zOh5UpykSo+s+h5vU9OjIKpRB1/FdO1cSm0+6jXYadvQ/dwHVbX83t1ad0dhok3xrq7T5NdRb3ia/WkkQBOZOBLhGBJMzpB19XU0hDzFXir0xuFgdxZBjomQFs5IMggttS2nWrSIUmYx3GNhg81Cz0CJyPKuygptRURU0mzgd9g6h3R2TqFBewtSmvVaswjMPlM+jrJRX4TSNIUXPq8CDSmud5+TDYLgtj/CuQdzD/IoN7bKorfcnqDdNvwYk7lKUb3ynxJLqwCwP9viaiK9+tT9q2QqahUSXC5EYcMFZ3q6ElsHr21MP73tlEIIw5juvn7n+yBMxYqARCYgoffI24MrSDlM25oIe5j+Niw095RaG7D8orDNDhlS659eGwg2b4vvG95K+uZmTcHu2vJBsFVje1HSxDnOhIB8ZZYIjDjDFQeEHmvk4DMp9cIHMy8Q8M4AU8T2NhE+9oybk+wPhq5HGHAR6U3SJjNKgwHoT2hIE6imAjmcHyYZ+ou6s4O8VXEm+kgKBRkVczZu7OT1nubirkGBhY3M1JJEc2i0I/ZP0EKkc/dimgDGphDM1XHsJ3aRiL0K2ToKJ6dwlqptaxvdvvxlp9x2mh4I9PdnYzDqYD131iMCCPyp8u5Ao/qh+TnlH/soUW7Bv2bSV2T0egnEykzImWr1O9UoC0X6UVxAG2U9vc6XdFKJkTxwcN3g4/kUcpUetDyCtBGwwNJlNU08jipSnbMJ7yDTyBOdChnDK3LGpOi2WQQ/SYwNkP3FMexrUDpDpJ26FaedgAF7VV/SQ4fhpRHc84EGUMGOtNsX6Mcqp28KZEm6bli4hSaTINDUIbEYYgfw8ooBmsU0EgIXnece6i6SEM+49c3qRVsLfGilrK6Xb9+lbrGle+myPbuej7NUQUyd7yFzipdioB8RzqQjiNFbYJ/nOXyiJvTgXWG5IDsuenbMxyH5C3TWv2LXMGLQ4+XB0qat/rUvNOdMaVvTvzSuvF7+N90nkCSCwA1SlRLR5jzMf7miPBU2EenNsW1nF+25Nt05N7z50IxksYZEkuiUhoUMKfEjci1fWIVqnehu6vzNt335T7ZloD/0raoW3yMzi/P5SMvm2JA3JOmFendBo6cnqzb7H7xeOaGuY+B4j/iFPCR1NSg/bP5dCX0kGaCZywVOiTavidTDHfN0PK6qIdVfazjIHDqivppYbBme0evatz6+nHVAxixE5aSNRSxTJV8So5jYSGGlVHU9OK/VbVcZvblUPiPhSEhIkBlrxkpZpZXQBOK8TmCDc2xM3/TzVoyZJgWTKwID1ESnzxwJZOHnOB1UOL3aghtifX1sBiI1NPGNWbX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**Steps to use the QnnContext\_createFromBinaryWithCallback API:** **1. Define Callback Functions** Implement the logic for buffer allocation together with context binary data loading. Users have to ensure that the following requirements are met for external buffers: > > > - When using Qnn\_ContextBinaryDmaBufferCallback\_t, they have to be DMA buffers. > - - The buffer’s start address must be aligned to at least 4KB (page alignment). It is highly recommended that the valid data begins exactly at the start of the external buffer (i.e., dataStartOffset should ideally be 0). This recommendation is based on two key considerations. > - - Starting from qairt-2.37, the context binary data format has been optimized to ensure that the main data section is 4KB-aligned relative to the file start, enabling efficient offset-based reads. > - The backend currently has limitations in handling arbitrary dataStartOffset value during the mapping phase. > - They must be at least the required size specified in the request parameter passed through the callback. > - For external DMA buffers, the FDs returned by the dataProvider callback must always be distinct—each invocation must return a different FD. > - After data loading, any modification to the external buffer should only be induced by QNN, otherwise behavior is undefined. > - They must not be deallocated until QNN explicitly invokes the dataRelease callback. **2. Create Context** Use the QnnContext\_createFromBinaryWithCallback API to create the context. > > > - The dataProvider and dataRelease callbacks are registered with the context. > - During context creation, QNN will invoke the registered dataProvider callback to allocate external buffers and load the required data.The callback might be invoked multiple times—once for each data section being loaded (e.g., shared weights and non-shared weights are handled separately). > - Once the data is loaded(callback returns), QNN will handle the mapping of external buffers to ensure they are shared with the backend. > - During context release, the dataRelease callback will be triggered to properly release the external buffers. ### Code example: HTP external weights-loaded buffer example 1// QnnInterface_t is defined in ${QNN_SDK_ROOT}/include/QNN/QnnInterface.h 2 QnnInterface_t qnnInterface; 3 // Init qnn interface ...... 4 // See ${QNN_SDK_ROOT}/examples/QNN/SampleApp code 5 6// Step 1. Define the DMA callback function 7Qnn_ErrorHandle_t dmaDataProviderFn(Qnn_ContextBinaryDataRequest_t req, 8 Qnn_ContextBinaryDmaDataResponse_t* dmaDataResponse, 9 void* notifyParam) { 10 // Implement buffer allocation and data loading processes 11 Qnn_ErrorHandle_t err = QNN_SUCCESS; 12 13 // notifyParam can be used to pass a custom instance for identifying which model to load. 14 std::pair* pair = reinterpret_cast*>(notifyParam); 15 CustomClass* CustomClass = pair->first; 16 uint32_t contextId = pair->second; 17 18 if (req.size == 0) { 19 // handle error 20 return QNN_GRAPH_ERROR_INVALID_ARGUMENT; 21 } 22 23 //allocate dma buffer 24 int32_t memFd = -1; 25 const uint64_t alignOptimizedBufferSize = getAlignedSizeInBytes(PAGE_ALIGNED_SIZE, req.size); 26 27 BufferInfo bufferInfo; 28 err = CustomClass->derectIOScheme->allocateDmaBuffer(CustomClass->m_filePath[contextId], req.offset, alignOptimizedBufferSize, &bufferInfo); 29 if (bufferInfo.addr == nullptr) { 30 // handle error 31 return QNN_CONTEXT_ERROR_MEM_ALLOC; 32 } 33 34 dmaDataResponse->dmaBuffer.data = bufferInfo.addr; 35 dmaDataResponse->dmaBuffer.fd = bufferInfo.dma_fd; 36 dmaDataResponse->dataStartOffset = bufferInfo.paddingSize; 37 dmaDataResponse->alignedSize = bufferInfo.alignedSize; 38 39 //loading data to dma buffer 40 err = CustomClass->derectIOScheme->storeBufferData(bufferInfo); 41 if (err != QNN_SUCCESS) { 42 // handle error 43 return QNN_CONTEXT_ERROR_MEM_ALLOC; 44 } 45 46 return err; 47} 48 49Qnn_ErrorHandle_t dmaDataReleaseFn(Qnn_ContextBinaryDmaDataMem_t dmaDataMem, 50 void* notifyParam) { 51 // Implement buffer release process 52 Qnn_ErrorHandle_t err = QNN_SUCCESS; 53 54 // free dma buffer 55 err = CustomClass->derectIOScheme->deallocateDmaBuffer(dmaDataMem); 56 if (err != QNN_SUCCESS) { 57 // handle error 58 } 59 60 return err; 61} 62 63// Step2. Create the context with QnnContext_createFromBinaryWithCallback API 64std::pair* notifyParam = 65 new std::pair(this, static_cast(contextIdx)); 66 67Qnn_ContextBinaryCallback_t callback { 68 .type = QNN_CONTEXT_CALLBACK_DMA_BUFFER, 69 .dmaBufferCallback = Qnn_ContextBinaryDmaBufferCallback_t { 70 QNN_CONTEXT_CALLBACK_DMA_BUFFER_VERSION_1, 71 Qnn_ContextBinaryDmaBufferCallbackV1_t {dmaDataProviderFn, 72 dmaDataReleaseFn, 73 static_cast(notifyParam)} 74 } 75}; 76 77Qnn_ContextHandle_t context; 78Qnn_ErrorHandle_t error = m_qnnFunctionPointers.qnnInterface.contextCreateFromBinaryWithCallback( 79 backend, 80 device, 81 config, 82 &callback, 83 binaryBuffer, 84 binaryBufferSize, 85 &context, 86 profile, 87 signal); 88 89if (error != QNN_SUCCESS) { 90// handle the error 91} 92 93// Execute graph 94// See ${QNN_SDK_ROOT}/examples/QNN/SampleApp code 95 96// Free context 97// See ${QNN_SDK_ROOT}/examples/QNN/SampleApp code for details 98if (QNN_CONTEXT_NO_ERROR != m_qnnFunctionPointers.qnnInterface.contextFree(context, profileBackendHandle)) { 99 // handle error 100} Copy to clipboard Last Published: Oct 10, 2025 [Previous Topic API Specializations](https://docs.qualcomm.com/bundle/publicresource/80-63442-50/topics/dsp_backend.md) [Next Topic HTA](https://docs.qualcomm.com/bundle/publicresource/80-63442-50/topics/hta_backend.md)