# Benchmarking

Overview

The benchmark shipped in the Qualcomm® Neural Processing SDK consists of a set of
python scripts that runs a network on a target
Android/LinuxEmbedded device and collect performance metrics.
It uses executables and libraries found in the SDK package to
run a DLC file on target, using a set of inputs for the
network, and a file that points to that set of inputs.

The input to the benchmark scripts is a configuration file in
JSON format. The SDK ships with a configuration file for
running the Inception\_v3 model that is created in the Qualcomm® Neural Processing SDK. SDK
users are encouraged to create their own configuration files
and use the benchmark scripts to run on target to collect
timing and memory consumption measurements.

The configuration file allows the user to specify:

- Name of the benchmark (i.e., Inception\_v3)
- Host path to use for storing results
- Device paths to use (where to push the necessary files for
running the benchmark)
- Device to run the benchmark on (only one device is supported
per run)
- Hostname/IP of remote machine to which devices are connected
- Number of times to repeat the run
- Model specifics (name, location of dlc, location of inputs)
- Qualcomm® Neural Processing SDK runtime configuration(s) to use (combination of CPU,
GPU, GPU\_FP16 and DSP)
- Which measurements to take (“mem” and/or “timing”)
- Profiling level of measurements (“off”, “basic”, “moderate”
or “detailed”)

Command Line Parameters

To see all of the command line parameters use the “-h” option
when running snpe\_bench.py

usage: snpe_bench.py [-h] -c CONFIG_FILE [-o OUTPUT_BASE_DIR_OVERRIDE]
                         [-v DEVICE_ID_OVERRIDE] [-r HOST_NAME] [-a]
                         [-t DEVICE_OS_TYPE_OVERRIDE] [-d] [-s SLEEP]
                         [-b USERBUFFER_MODE] [-p PERFPROFILE] [-l PROFILINGLEVEL]
                         [-json] [-cache]
    
    Run the snpe_bench
    
    required arguments:
      -c CONFIG_FILE, --config_file CONFIG_FILE
                            Path to a valid config file
                            Refer to sample config file config_help.json for more
                            detail on how to fill params in config file
    
    optional arguments:
      -o OUTPUT_BASE_DIR_OVERRIDE, --output_base_dir_override OUTPUT_BASE_DIR_OVERRIDE
                            Sets the output base directory.
      -v DEVICE_ID_OVERRIDE, --device_id_override DEVICE_ID_OVERRIDE
                            Use this device ID instead of the one supplied in config
                            file. Cannot be used with -a
      -r HOST_NAME, --host_name HOST_NAME
                            Hostname/IP of remote machine to which devices are
                            connected.
      -a, --run_on_all_connected_devices_override
                            Runs on all connected devices, currently only support 1.
                            Cannot be used with -v
      -t DEVICE_OS_TYPE_OVERRIDE, --device_os_type_override DEVICE_OS_TYPE_OVERRIDE
                            Specify the target OS type, valid options are
                            ['android-aarch64', 'le_oe_gcc8.2', 'le64_oe_gcc8.2']
      -d, --debug           Set to turn on debug log
      -s SLEEP, --sleep SLEEP
                            Set number of seconds to sleep between runs e.g. 20
                            seconds
      -b USERBUFFER_MODE, --userbuffer_mode USERBUFFER_MODE
                            [EXPERIMENTAL] Enable user buffer mode, default to
                            float, can be tf8exact0
      -p PERFPROFILE, --perfprofile PERFPROFILE
                            Set the benchmark operating mode (balanced, default,
                            sustained_high_performance, high_performance,
                            power_saver, low_power_saver, high_power_saver,
                            extreme_power_saver, low_balanced, system_settings)
      -l PROFILINGLEVEL, --profilinglevel PROFILINGLEVEL
                            Set the profiling level mode (off, basic, moderate, detailed, linting).
                            Default is basic.
      -json, --generate_json
                            Set to produce json output.
      -cache, --enable_init_cache
                            Enable init caching mode to accelerate the network
                            building process. Defaults to disable.
    Copy to clipboard

Running the Benchmark

Prerequisites

- The Qualcomm® Neural Processing SDK has been set up following the [Qualcomm (R) Neural Processing SDK
Setup](https://docs.qualcomm.com/doc/80-63442-10/topic/SNPE_general_setup.html) chapter.
- The [Tutorials Setup](https://docs.qualcomm.com/doc/80-63442-10/topic/tutorial_setup.html) has been
completed.
- **Optional:** If the device is connected to remote machine,
the remote adb server setup needs to be done by the user.

Running Inceptionv3 that is Shipped with the SDK

snpe\_bench.py is the main benchmark script to measure and
report performance statistics. Here is how to use it with the
Inception\_v3 model and data that is created in the SDK.

cd $SNPE_ROOT/benchmarks/SNPE
    python3 snpe_bench.py -c inception_v3_sample.json -a
    
        where
            -a benchmarks on the device connected
    Copy to clipboard

Viewing the Results (csv File or json File)

All results are stored in the “HostResultDir” that is specified
in the configuration json file. The benchmark creates
time-stamped directories for each benchmark run. All timing
results are stored in microseconds.

For your convenience, a “latest\_results” link is created that
always points to the most recent run.

# In inception_v3_sample.json, "HostResultDir" is set to "inception_v3/results"
    cd $SNPE_ROOT/benchmarks/SNPE/inception_v3/results
    # Notice the time stamped directories and the "latest_results" link.
    cd $SNPE_ROOT/benchmarks/SNPE/inception_v3/results/latest_results
    # Notice the .csv file, open this file in a csv viewer (Excel, LibreOffice Calc)
    # Notice the .json file, open the file with any text editor
    Copy to clipboard

**CSV Benchmark Results File**

The CSV file contains results similar to the example below.
Some measurements may not be apparent in the CSV file. To get
all timing information, profiling level needs to be set to
detailed. By default, profiling level is basic. Note that
colored headings have been added for clarity.

![../images/csv_results.png](data:image/png;base64,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)

**CSV 1: Configuration**

This section contains information about:

- the SDK version used to generate the benchmark run
- model name and path to the DLC file
- runtimes selected for the benchmark
- etc.

**CSV 2: Initialization metrics**

This section contains measurements concerning model
initialization. Profiling level affects the amount of
measurements collected. No metrics are collected for profiling
off. Metrics for basic and detailed are shown below.

Profiling level: Basic

- Load measures the time required to load the model’s
metadata.
- Deserialize measures the time required to deserialize the
model’s buffers.
- Create measures the time spent to create Qualcomm® Neural Processing SDK network(s) and
initialize all layers for the given model. Detailed
breakdown of create time can be retrieved with profiling
level set to detailed.
- Init measures the time taken to build and configure Qualcomm® Neural Processing SDK.
This time includes time measured Load, Deserialize, and
Create.
- De-Init measures the time taken to de-initialized Qualcomm® Neural Processing SDK.

Profiling level: Moderate or Detailed

- Load measures the time required to load the model’s
metadata.
- Deserialize measures the time required to deserialize the
model’s buffers.
- Create measures the time spent to create Qualcomm® Neural Processing SDK network(s) and
initialize all layers for the given model. Detailed
breakdown of create time can be retrieved with profiling
level set to detailed.
- Init measures the time taken to build and configure Qualcomm® Neural Processing SDK.
This time includes time measured Load, Deserialize, and
Create.
- De-Init measures the time taken to de-initialized Qualcomm® Neural Processing SDK.
- Create Network(s) measures the total time required to create
all network(s) for the model. Models that are partitioned
will result in multiple networks being created.
- RPC Init Time measures the entire time spent on RPC and
accelerators used by Qualcomm® Neural Processing SDK. This time includes time measured
in Snpe Accelerator Init Time and Accelerator Init Time.
Currently only available for DSP and AIP runtime. Will
appear as 0 for other runtimes.
- Snpe Accelerator Init Time measures the total time spent by
Qualcomm® Neural Processing SDK to prepare the data for the accelerator process such as
GPU, DSP, AIP. Currently only available for DSP and AIP
runtime. Will appear as 0 for other runtimes.
- Accelerator Init Time measures the total processing time
spent on the accelerator core, which may include different
hardware resources. Currently only available for DSP and AIP
runtime. Will appear as 0 for other runtimes.

**CSV 3: Execution metrics**

This section contains measurements concerning the execution of
one inference pass of the neural network model. Profiling level
affects the amount of measurements collected.

Profiling level: Basic

- Total Inference Time measures the entire execution time of
one inference pass. This includes any input and output
processing, copying of data, etc. This is measured at the
start and end of the **execute** call.

Profiling level: Moderate or Detailed

- Total Inference Time measures the entire execution time of
one inference pass. This includes any input and output
processing, copying of data, etc. This is measured at the
start and end of the **execute** call.
- Forward Propogate measures the time spent executing one
inference pass excluding processing overheads on one of the
accelerator cores. For example, in the case of the GPU this
represents the execution time of all the GPU kernels running
on the GPU HW.
- RPC Execute measures the entire time spent on RPC and
accelerators used by Qualcomm® Neural Processing SDK. This time includes time measured
in Snpe Accelerator and Accelerator. Currently only
available for DSP and AIP runtime. Will appear as 0 for
other runtimes.
- Snpe Accelerator measures the total time spent by Qualcomm® Neural Processing SDK to
setup processing for the accelerator. Currently only
available for DSP and AIP runtime. Will appear as 0 for
other runtimes.
- Accelerator measures the total execution time spent on the
accelerator core, which may include different hardware
resources. Currently only available for DSP and AIP runtime.
Will appear as 0 for other runtimes.

Profiling level: Detailed

- Misc Accelerator measures the total execution time spent on
the accelerator core for optimization elements not specified
by Qualcomm® Neural Processing SDK. For example, in the case of DSP this represents the
exection time spent on additional layers added by the
accelerator to acheive optimal performance. Currently only
available for DSP and AIP runtime. Will appear as 0 for
other runtimes.

Profiling level: Linting

- Linting profiling mode is an HTP exclusive configuration that provides per op
cycle count on the main thread as well as background execution information.
[See here](https://docs.qualcomm.com/doc/80-63442-10/topic/linting_profile.html) for more information.

**CSV 4: Model Layer Names**

This section contains the list of names of each layer of the
neural network model.

Note that this information will only be present for DSP/GPU
runtime only if the profiling level is set to detailed.

**CSV 5: Model Layer Times**

Each row in this section represents the execution time of each
layer of the neural network model. For each runtime the
average, min and max values are represented. Note that the
values corresponding to each layer are referring to cycle
counts, whereas the values for all other components like init,
ForwardPropagate are in microseconds.

Note that this information will only be present for DSP/GPU
runtime only if the profiling level is set to detailed.

Refer to [Measurement
Methodology](https://docs.qualcomm.com/doc/80-63442-10/topic/general_benchmarking.html#measurement_methodology)
Measurement Methodology for more background on the numbers.

**JSON Benchmark Results File**

The benchmark results published in the CSV file can also be
made available in JSON format. The contents are the same as in
the CSV file, structured as key-value pairs, and will help
parsing the results in a simple and efficient manner. The JSON
file contains results similar to the example below.

![../images/json_results.png](data:image/png;base64,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)

Running the Benchmark with Your Own Network and
Inputs

**Prepare inputs**

Before running the benchmark, a set of inputs need to be ready:

- *your\_model.dlc*
- A text file listing all of your input data. For an example,
see: $SNPE\_ROOT/examples/Models/InceptionV3/data/cropped/raw\_list.txt
- All of the input data that is listed in the above text file.
For an example, see directory
$SNPE\_ROOT/examples/Models/InceptionV3/data/cropped
- Note: image\_list.txt must exactly match the structure of
your input directory.
- For more information on preparing your inputs, see: [Input
Images](https://docs.qualcomm.com/doc/80-63442-10/topic/image_input.html)

Create a Run Configuration

**Config structure**

The configuration file is a JSON file with a predefined
structure. Refer to $SNPE\_ROOT/benchmarks/SNPE/inception\_v3\_sample.json
as an example.

All fields are required.

- **Name:** The name of this configuration, e.g., Inception\_v3
- **HostRootPath:** The top level output folder on the host.
It can be an absolute path or a relative path to the current
working directory.
- **HostResultDir:** The folder on the host where all
benchmark results are put. It can be an absolute path or a
relative path to the current working directory.
- **DevicePath:** The folder on the device where all
benchmark-related data and artifacts are put, e.g.,
/data/local/tmp/snpebm
- **Devices:** The serial number of the device that the
benchmark runs on. Only one device is currently supported.
- **Runs:** The number of times that the benchmark runs for
each of the “Runtime” and “Measurements” run combinations
- **Model:**

    - **Name:** The name of the DNN model, e.g., INCEPTION\_V3
    - **Dlc:** The folder where the model dlc file is located
on the host. It can be an absolute path or a relative
path to the current working directory.
    - **InputList:** The text file path that lists all of the
input data. It can be an absolute path or a relative path
to the current working directory.
    - **Data:** A list of data files or folders that are listed
in InputList file. It can be an absolute path or a
relative path to the current working directory. If the
path is a folder, all contents of that folder will be
pushed to the device.
- **Runtimes:** Possible values are “GPU”, “GPU\_FP16”, “DSP”
and “CPU”. You can use any combination of these.
- **Measurements:** Possible values are “timing” and “mem”.
You can set either one or both of these values. Each
measurement type is measured alone for each run.

Optional fields:

- **CpuFallback:** Possible values are true and false.
Indicates whether or not the network can fallback to CPU
when a layer is not available on a runtime. Default value is
false.
- **HostName:** Hostname/IP of remote machine to which devices
are connected. Default value is ‘localhost’.
- **BufferTypes:** List of user buffertypes. Possible values
are “”(empty string), or any combination of “ub\_float” and
“ub\_tf8”. If “” is given, it executes for all the runtimes
given in RunTimes field. If any userbuffer option is given,
it executes for all runtimes in RunTimes along with given
userbuffer variants. If this field is absent, it is executed
for all possible runtimes(default case).

**Architecture support**

In Android, AARCH 64-bit is supported. In
addition to Android, there is limited support for
LinuxEmbedded, where only the timing measurement is supported.

Runtime and measurement are concatenated to make a full run
combination name, e.g.,

- “GPU\_timing”: GPU runtime, timing measurement
- “CPU\_mem”: CPU runtime, memory measurement

Note that for each specified runtime, there are multiple sets
of timing measurements which differ only in the tensor format
([Using
ITensors](https://docs.qualcomm.com/doc/80-63442-10/topic/cplus_plus_tutorial.html#cpp_tutorial_load_input_itensors)
and [Using User
Buffers](https://docs.qualcomm.com/doc/80-63442-10/topic/cplus_plus_tutorial.html#cpp_tutorial_load_input_user_buffers)).
For example, for the DSP runtime, the following timing
measurements are taken:

- “DSP\_timing”: Timing measurement on DSP runtime using
ITensors
- “DSP\_ub\_tf8\_timing”: Timing measurement on DSP runtime using
UserBuffer with TF8 encoding
- “DSP\_ub\_float\_timing”: Timing measurement on DSP runtime
using UserBuffer with float encoding

Run the Benchmark

The easiest way to run the benchmark is to specify the -a
option, which will run the benchmark on the lone device
connected to your computer.

cd $SNPE_ROOT/benchmarks/SNPE
    python3 snpe_bench.py -c yourmodel.json -a
    Copy to clipboard

The benchmark will do an md5sum on the host files (those
specified in the JSON configuration) and on the device files.
Because of the md5sum check, files needed for the benchmark run
has to be available on the host.

For any file that exists both on host and on the device with
mismatch md5, the benchmark will copy the file from host to
target and issue a warning message letting you know that the
local files do not match the device files. This is done so that
you can be sure that the results you get from a benchmark run
accurately reflect the files specified in the JSON file.

**Other options**

**-v option**

Allows you to override the device ID specified in the config
file, so that the same config file can be used across
multiple devices. You can use this instead of -a, if you have
multiple devices attached.

**-o option**

Result output base directory override applies only if
relative paths are specified for HostRootPath and
HostResultsDir. It allows you to pool the output regardless
of where you run the benchmark from.

**-t option**

OS Type override currently supports Android aarch64 (arm64-v8a), and LinuxEmbedded
devices.

**-b option**

Allows you to specify type of input and output user buffers.

**-p option**

Allows you to profile performance in different operating
modes.

**-l option**

Allows you to specify the level of performance profiling.

Reading the Results

Open the results(CSV file or JSON file) in your
&lt;HostResultsDir&gt;/latest\_results folder to view your results.
(&lt;HostResultsDir&gt; is what you specified in your json
configuration file.)

Measurement Methodology

In all cases, the snpe-net-run executable is used to load a
model and run inputs through the model.

**Performance (“timing”)**

Timing measurements are taken using internal timing utilities
inside the Qualcomm® Neural Processing SDK libraries. When snpe-net-run is executed, the
libraries will log timing info to a file. This file is then
parsed offline to retrieve total inference times and per-layer
times.

The total inference times include both the per-layer
computation times plus overhead such as data movements between
layers, as well as into and out of runtimes, whereas the
per-layer times are strictly computational times for each
layer. For smaller networks the overhead can be quite
significant relative to computational time, particularly when
offloading the networks to run on GPU or DSP.

As well, further optimizations present on the GPU/DSP may cause
layer times to be mis-attributed, in the case of neuron
conv-neuron or fc-neuron pairs. When executing on GPU the total
time of the pairs would be assigned to convs, whereas for DSP
they would be assigned to the neurons.

Note

Detailed and Linting Profiling will cause performance impact.

Last Published: Jun 04, 2026

[Previous Topic
Benchmarking and Accuracy](https://docs.qualcomm.com/bundle/publicresource/80-63442-10/topics/usergroup10.md) [Next Topic
Linting Profile](https://docs.qualcomm.com/bundle/publicresource/80-63442-10/topics/linting_profile.md)