# SNPE1 to SNPE2 Migration Guide

There are 2 primary changes that drove the major version update
to SNPE2:

- C API

    - SNPE2 introduces a C API.
    - This API will be the only way to interact with Qualcomm® Neural Processing SDK going
forward.
    - The C API reduces/removes potential compatibility
concerns that C++ can have (e.g. due to C++ templates).
    - To ease the transition, the C++ API will continue to be
supported until early/mid 2023, at which point it will be
retired.
    - For more information, reference the “C tutorial” section
of “Code Examples” and C API sections.
- DLC Content Changes

    - The contents of the DLC file are being updated.
    - These changes mean that the DLC is not compatible between
SNPE1 and SNPE2.
    - All DLCs must be generated for SNPE2.
    - HTP Offline Cache Records are backward compatible in
SNPE2 - a cache generated with an older SNPE2 version
will work in a later SNPE2 version.
    - Offline Caches in SNPE2 are compatible across SoCs:

        - For the same DSP Arch, a cache prepared for one SoC
can run on another SoC if the “prepared with VTCM” is
less than or equal to the “target SoC VTCM”. For
example, a cache prepared for sm7450 with 2MB VTCM is
compatible with sm8450 which has 8MB VTCM, but a cache
prepared for sm8450 with 4MB VTCM will not be
compatible with sm7450 which only has 2MB VTCM.
        - A cache generated for a newer DSP Arch cannot run on
an SoC with a lower DSP Arch. A cache generated for
sm8550 (v73) will not be compatible with sm8450 (v69),
whereas a cache generated for sm8450 (v69) will be
compatible with sm8550 (v73).
    - DLCs can now represent additional datatypes not supported
by SNPE1 – cardinal datatypes (int8, int32, uint32), fp16.

        - These are in addition to the fp32 and quantized8 bit
types that were supported in SNPE1.
        - Any User Buffers will need to match the data types
contained in the DLC.

            - snpe-dlc-info can be used to identify the proper
types.
    - Additionally, the names of layers and input/output
tensors may be assigned different names than in SNPE1.

        - snpe-dlc-info can be used to identify and review what
names the converters generate.
        - Inputs (generally referred as “Data” layer types) and
“Const” layer types are now folded into the consuming
Ops and not considered as standalone “layers”. As a
result, they will no longer be visible in the output
generated by snpe-diagview and snpe-dlc-info tools.
        - If client code uses APIs like
SNPEBuilder.setOutputTensors() to request specific
output tensors, this code may have to be modified as a
result.
    - ArgMax now always outputs a uint32, so there may be some
performance impact if the SNPE1 based model used an int8
output, due to the larger data size.

        - Previously, SNPE1 would use int8 if the maximum value
was less than or equal to 255.

SDK Behavior/Content Changes:

- Unsigned PD/Skels

    - SNPE2 defaults to using Unsigned PD.
    - SNPE2 delivers unsigned skel files.
    - In order to use a Signed PD, the skels must be signed by
the customer, and the signed PD must be explicitly
requested at runtime.
- Runtime Changes:

    - The DSP runtime for SOCs with V66 CDSP does not support
running a float DLC. The DLC must be quantized to be used
with the DSP runtime for these SOCs.
    - For the GPU runtime, some of the initialization time is
recorded differently, so init times will appear longer.
This is simply a difference in how the initialization is
measured and captured.
- Integer Input/Output Tensors

    - SNPE1 did not support integer (cardinal) tensor types.
Indices or integer data were handled as floating point
tensors or using Q8 input using a special hard-wired
quantization encoding [offset=0, scale=1.0].
    - SNPE2 supports integer type directly. The
converter/quantizer will represent integer input as a
UIntN tensor, where N can be 8, 16, or 32.
    - Qualcomm® Neural Processing SDK cannot convert a Q8 input tensor to UInt8 input
tensor, so any existing code for such a network will have
to be modified to use the new API to create UInt8
UserBuffer input.
    - Qualcomm® Neural Processing SDK can convert a floating-point input tensor to UInt8
input tensor, but this code path will be slower than
providing correct type.
- Tool/Flag Changes:

    - The snpe-dlc-quantize functionality has been refactored
into two separate tools:

        - A shell script is still provided that supports the
previous command-line interface, and calls the new
tools as appropriate.
        - snpe-dlc-quant: for generating the quantized DLCs from
float DLC, with various quantization options.
        - snpe-dlc-graph-prepare: for performing offline graph
preparation on quantized DLCs for HTP SOCs.

            - This enables the flexibility to re-prepare the
graph quickly from quantized DLC if needed, without
needing to do both quantize and offline prepare
which generally takes more time.
        - Flag Changes:

            - The “bc” (bias correction) algorithm is deprecated,
and has no effect.
            - The “enable\_hta” option is deprecated and has no
effect.

                - Offline preparation for AIP is no longer
supported, and all preparation is done at
runtime.
    - snpe-net-run:

        - A new flag “–userbuffer\_auto” has been added to
automatically detect and create the right userbuffer
type based on input and output tensor data type of the
model.

SDK Content Changes:

- The snpe-dlc-reorder tool has been removed, as it is no
longer relevant (it was only used for HTA offline prepare)

Last Published: Oct 02, 2025

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