# Saver Tutorial: Save execution sequence with Saver and replay on a backend

This tutorial describes how to use the QNN Saver backend to capture the execution sequence of
QNN APIs. The captured output can then be compiled and replayed on any QNN backend.

## Linux

### Generating saver\_output.c

saver\_output.c is an artifact produced by running a model on the Saver backend. A model in a source
framework can be converted into a `model.cpp` and `model.bin` using the
[QNN Converters](https://docs.qualcomm.com/doc/80-63442-50/topic/converters.html). Sample `model.cpp` and `model.bin` files are located in
`${QNN_SDK_ROOT}/examples/QNN/converter/models/`.

`model.cpp` and `model.bin` are used to create a `model.so` via `qnn-model-lib-generator`.

$ ${QNN_SDK_ROOT}/bin/x86_64-linux-clang/qnn-model-lib-generator \
                  -c ${QNN_SDK_ROOT}/examples/QNN/converter/models/qnn_model_float.cpp \
                  -b ${QNN_SDK_ROOT}/examples/QNN/converter/models/qnn_model_float.bin \
                  -o ${QNN_SDK_ROOT}/examples/QNN/converter/model_libs # This can be any path
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This will produce the following artifacts:

- `${QNN_SDK_ROOT}/examples/QNN/converter/model_libs/aarch64-android/libqnn_model_float.so`
- `${QNN_SDK_ROOT}/examples/QNN/converter/model_libs/x86_64-linux-clang/libqnn_model_float.so`

The resulting `model.so` can be run on the Saver backend using `qnn-net-run`:

$ cd ${QNN_SDK_ROOT}/examples/QNN/converter/models/
    $ ${QNN_SDK_ROOT}/bin/x86_64-linux-clang/qnn-net-run \
                  --backend ${QNN_SDK_ROOT}/lib/x86_64-linux-clang/libQnnSaver.so \
                  --model ${QNN_SDK_ROOT}/examples/QNN/converter/model_libs/x86_64-linux-clang/libqnn_model_float.so \
                  --input_list input_list_float.txt
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This will produce the following artifacts:

- `./saver_output/saver_output.c`
- `./saver_output/params.bin`

See Tutorial: Converting and executing a CNN model with QNN for more information on converting a model from a source framework
into the QNN shared library format.

Alternatively, the following script performs the steps described above and can be used for
convenience.

$ cd ${QNN_SDK_ROOT}/examples/QNN/NetRun/linux-x86_64
    $ ./linux-qnn-net-run.sh -b saver
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This will produce a quantized `saver_output.c` and `params.bin` in
`${QNN_SDK_ROOT}/examples/QNN/NetRun/linux-x86_64/saver_output/quantized/` for replay on DSP or HTP
backends, and a non-quantized `saver_output.c` and `params.bin` in
`${QNN_SDK_ROOT}/examples/QNN/NetRun/linux-x86_64/saver_output/non_quantized/` for replay on CPU or GPU
backends.

### Compiling saver\_output.c

To compile saver\_output.c for replay on a backend, use the `Makefile` located in
`${QNN_SDK_ROOT}/examples/QNN/Saver`. This makefile can be used to compile saver\_output.c for the
specified QNN backend on a compatible platform.

Note

- Compiling for x86 targets requires clang.
- Compiling for Android targets requires Android NDK.
- Compiling for DSP/HTP backends on Android targets requires Hexagon SDK.

Refer to [Setup](https://docs.qualcomm.com/doc/80-63442-50/topic/setup.html) to set the appropriate environment variables.

With `saver_output.c` located in the same directory as `Makefile`, run `make all` to compile
for all QNN backends on supported targets.

$ cp ./saver_output/saver_output.c ${QNN_SDK_ROOT}/examples/QNN/Saver
    $ cp ./saver_output/params.bin ${QNN_SDK_ROOT}/examples/QNN/Saver
    $ cd ${QNN_SDK_ROOT}/examples/QNN/Saver
    $ make x86 android
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This will produce a folder for each target:

- `./x86_64-linux-clang`
- `./aarch64-android`

These folders will contain one executable for each backend available on the target named
`saver_output_<backend>`.

To create executables for all backends on a specific target, run `make <target>`. Valid options
for &lt;target&gt; are **x86** or **aarch64**.

To create an executable for a specific backend on a specific target, run
`make <backend>_<target>`. Valid options for &lt;backend&gt; are **cpu** or **htp** for &lt;target&gt;=x86,
and **cpu**, **gpu**, **dsp**, **hta**, or **htp** for &lt;target&gt;=aarch64.

### Replaying saver\_output.c on x86

To replay on x86 backends, set LD\_LIBRARY\_PATH to point to the desired QNN backend library and run
the executable. Ensure `params.bin` is in the current working directory.

$ cd ${QNN_SDK_ROOT}/examples/QNN/Saver
    $ export QNN_BACKEND=<backend> # where <backend> is QnnCpu or QnnHtp
    $ export LD_LIBRARY_PATH=${QNN_SDK_ROOT}/lib/x86_64-linux-clang:$LD_LIBRARY_PATH
    $ ./x86_64-linux-clang/saver_output_${QNN_BACKEND} [--logging <verbose,debug,info,warn,error>] # optionally enable logging
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### Replaying saver\_output.c on Android

#### Replay on QNN CPU, QNN HTA, or QNN GPU Backend

Make a directory on device, push QNN backend library, `saver_output.c` executable, and
`params.bin`:

$ export QNN_BACKEND=<backend> # where <backend> is QnnCpu, QnnHta, or QnnGpu
    $ adb shell "mkdir /data/local/tmp/saver"
    $ adb push ${QNN_SDK_ROOT}/lib/aarch64-android/lib${QNN_BACKEND}.so /data/local/tmp/saver
    $ adb push ${QNN_SDK_ROOT}/examples/QNN/Saver/aarch64-android/saver_output_${QNN_BACKEND} /data/local/tmp/saver
    $ adb push ./saver_output/params.bin /data/local/tmp/saver
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Set LD\_LIBRARY\_PATH on device and execute

$ adb shell
    $ cd /data/local/tmp/saver
    $ export LD_LIBRARY_PATH=/data/local/tmp/saver:$LD_LIBRARY_PATH
    $ ./saver_output_${QNN_BACKEND} [-l <verbose,debug,info,warn,error>] # optionally enable logging
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#### Replay on QNN DSP or QNN HTP Backend

Make a directory on device, push QNN backend stub/skel, `saver_output.c` executable, and
`params.bin`:

$ export QNN_BACKEND=<backend>  # where <backend> is QnnDsp or QnnHtp
    $ export HEXAGON_ARCH=<v65,v66,v68,v69,v73,v75>  # based on device
    $ adb shell "mkdir data/local/tmp/saver"
    $ adb push ${QNN_SDK_ROOT}/lib/aarch64-android/lib${QNN_BACKEND}.so /data/local/tmp/saver
    $ adb push ${QNN_SDK_ROOT}/lib/aarch64-android/lib${QNN_BACKEND}${HEXAGON_ARCH^}Stub.so /data/local/tmp/saver
    $ adb push ${QNN_SDK_ROOT}/lib/hexagon-${HEXAGON_ARCH}/unsigned/lib${QNN_BACKEND}${HEXAGON_ARCH^}Skel.so /data/local/tmp/saver
    $ adb push ${QNN_SDK_ROOT}/lib/aarch64-android/libQnnHtpPrepare.so /data/local/tmp/saver  # if QNN_BACKEND=QnnHtp
    $ adb push ${QNN_SDK_ROOT}/examples/QNN/Saver/aarch64-android/saver_output_${QNN_BACKEND} /data/local/tmp/saver
    $ adb push ${QNN_SDK_ROOT}/examples/QNN/Saver/params.bin /data/local/tmp/saver
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Set LD\_LIBRARY\_PATH and ADSP\_LIBRARY\_PATH on device and execute:

$ adb shell
    $ cd /data/local/tmp/saver
    $ export LD_LIBRARY_PATH=/data/local/tmp/saver:$LD_LIBRARY_PATH
    $ export ADSP_LIBRARY_PATH=/data/local/tmp/saver:$ADSP_LIBRARY_PATH
    $ ./saver_output_${QNN_BACKEND} [-l <verbose,debug,info,warn,error>] # optionally enable logging
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## Windows

### Setup

The tutorial assumes general setup instructions have been followed at [Setup](https://docs.qualcomm.com/doc/80-63442-50/topic/setup.html).

Please use “x86\_x64 Cross Tools Command Prompt for VS 2022” to set QNN\_SDK\_ROOT:

$ set QNN_SDK_ROOT=\path\to\QNN_SDK_ROOT
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### Build

Please use “x86\_x64 Cross Tools Command Prompt for VS 2022” in this step.

#### Generate Model

For CPU, generate a non-quantized model:

**For Windows native/x86\_x64 PC developers**

$ cd "%QNN_SDK_ROOT%\examples\QNN\Saver"
    $ mkdir model && cd model
    $ xcopy "%QNN_SDK_ROOT%\examples\QNN\converter\models\qnn_model_float.cpp" .
    $ xcopy "%QNN_SDK_ROOT%\examples\QNN\converter\models\qnn_model_float.bin" .
    $ py -3 "%QNN_SDK_ROOT%\bin\x86_64-windows-msvc\qnn-model-lib-generator" ^
               -c .\qnn_model_float.cpp ^
               -b .\qnn_model_float.bin ^
               -o .\model_libs ^
               -t windows-x86_64
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After executing commands from above, you should be able to see:

- `%QNN_SDK_ROOT%\examples\QNN\Saver\model\model_libs\x64\qnn_model_float.dll`

**For Windows on Snapdragon developers**

$ cd "%QNN_SDK_ROOT%\examples\QNN\Saver"
    $ mkdir model && cd model
    $ xcopy "%QNN_SDK_ROOT%\examples\QNN\converter\models\qnn_model_float.cpp" .
    $ xcopy "%QNN_SDK_ROOT%\examples\QNN\converter\models\qnn_model_float.bin" .
    $ py -3 "%QNN_SDK_ROOT%\bin\aarch64-windows-msvc\qnn-model-lib-generator" ^
               -c .\qnn_model_float.cpp ^
               -b .\qnn_model_float.bin ^
               -o .\model_libs ^
               -t windows-aarch64
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After executing commands from above, you should be able to see:

- `%QNN_SDK_ROOT%\examples\QNN\Saver\model\model_libs\ARM64\qnn_model_float.dll`

For DSP and HTP, generate a quantized model:

**For Windows native/x86\_x64 PC developers**

$ cd "%QNN_SDK_ROOT%\examples\QNN\Saver"
    $ mkdir model && cd model
    $ xcopy "%QNN_SDK_ROOT%\examples\QNN\converter\models\qnn_model_8bit_quantized.cpp" .
    $ xcopy "%QNN_SDK_ROOT%\examples\QNN\converter\models\qnn_model_8bit_quantized.bin" .
    $ py -3 "%QNN_SDK_ROOT%\bin\x86_64-windows-msvc\qnn-model-lib-generator" ^
               -c .\qnn_model_8bit_quantized.cpp ^
               -b .\qnn_model_8bit_quantized.bin ^
               -o .\model_libs ^
               -t windows-x86_64
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After executing commands from above, you should be able to see:

- `%QNN_SDK_ROOT%\examples\QNN\Saver\model\model_libs\x64\qnn_model_8bit_quantized.dll`

**For Windows on Snapdragon developers**

$ cd "%QNN_SDK_ROOT%\examples\QNN\Saver"
    $ mkdir model && cd model
    $ xcopy "%QNN_SDK_ROOT%\examples\QNN\converter\models\qnn_model_8bit_quantized.cpp" .
    $ xcopy "%QNN_SDK_ROOT%\examples\QNN\converter\models\qnn_model_8bit_quantized.bin" .
    $ py -3 "%QNN_SDK_ROOT%\bin\aarch64-windows-msvc\qnn-model-lib-generator" ^
               -c .\qnn_model_8bit_quantized.cpp ^
               -b .\qnn_model_8bit_quantized.bin ^
               -o .\model_libs ^
               -t windows-aarch64
    Copy to clipboard

After executing commands from above, you should be able to see:

- `%QNN_SDK_ROOT%\examples\QNN\Saver\model\model_libs\ARM64\qnn_model_8bit_quantized.dll`

#### Generate saver\_output.c and params.bin

Please check the path of &lt;model&gt;.dll and replace in the below commands:

$ cd "%QNN_SDK_ROOT%\examples\QNN\Saver"
    $ mkdir source && cd source
    $ xcopy "%QNN_SDK_ROOT%\examples\QNN\converter\models\input_data_float" .\input_data_float /i
    $ xcopy "%QNN_SDK_ROOT%\examples\QNN\converter\models\input_list_float.txt" .
    $ xcopy "%QNN_SDK_ROOT%\examples\QNN\Saver\model\model_libs\x64\<model>.dll" .
    $ "%QNN_SDK_ROOT%\bin\x86_64-windows-msvc\qnn-net-run.exe" ^
               --backend "%QNN_SDK_ROOT%\lib\x86_64-windows-msvc\QnnSaver.dll" ^
               --model <model>.dll ^
               --input_list .\input_list_float.txt
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After executing commands from above, you should be able to see:

- `%QNN_SDK_ROOT%\examples\QNN\Saver\source\saver_output\saver_output.c`
- `%QNN_SDK_ROOT%\examples\QNN\Saver\source\saver_output\params.bin`

#### Build saver\_output.c

Please choose one target between [x64, ARM64] as the parameter of “-A” config.
Executables for all backends on the target named `saver_output_<backend>` will be generated.

$ cd "%QNN_SDK_ROOT%\examples\QNN\Saver"
    $ xcopy "%QNN_SDK_ROOT%\examples\QNN\Saver\source\saver_output\saver_output.c" .
    $ mkdir build && cd build
    $ cmake -S ../ -B ./ -T ClangCL -A [x64, ARM64]
    $ cmake --build ./ --config Release
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To generate an executable for a specific backend, please use below command to build.
Valid options for &lt;backend&gt; are cpu for &lt;target&gt;=x64, and cpu, dsp, or htp for &lt;target&gt;=ARM64.

$ cmake --build ./ --config Release --target [cpu, dsp, htp]
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After executing commands from above, you should be able to see:

- `%QNN_SDK_ROOT%\examples\QNN\Saver\build\Release\saver_output_<backend>.exe`

### Run

Please use “Command Prompt” to execute, and ensure the `params.bin` is the same file generated in [build](https://docs.qualcomm.com/doc/80-63442-50/topic/saver_tutorial.html#build).

#### For CPU

Run on x86.

Please push below files to a folder:

- `%QNN_SDK_ROOT%\examples\QNN\Saver\build\Release\saver_output_QnnCpu.exe`
- `%QNN_SDK_ROOT%\lib\x86_64-windows-msvc\QnnCpu.dll`
- `%QNN_SDK_ROOT%\examples\QNN\Saver\source\saver_output\params.bin`

To execute:

$ .\saver_output_QnnCpu.exe [--logging <verbose,debug,info,warn,error>] # optionally enable logging
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Run on aarch64.

Please push below files to device:

> 
> 
> - `%QNN_SDK_ROOT%\examples\QNN\Saver\build\Release\saver_output_QnnCpu.exe`
> - `%QNN_SDK_ROOT%\lib\aarch64-windows-msvc\QnnCpu.dll`
> - `%QNN_SDK_ROOT%\examples\QNN\Saver\source\saver_output\params.bin`

To execute:

$ .\saver_output_QnnCpu.exe [--logging <verbose,debug,info,warn,error>] # optionally enable logging
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#### For DSP

Run on aarch64.

Please push below files to device:

- `%QNN_SDK_ROOT%\examples\QNN\Saver\build\Release\saver_output_QnnDsp.exe`
- `%QNN_SDK_ROOT%\lib\aarch64-windows-msvc\QnnDsp.dll`
- `%QNN_SDK_ROOT%\lib\aarch64-windows-msvc\QnnDspV66Stub.dll`
- `%QNN_SDK_ROOT%\lib\hexagon-v66\unsigned\libQnnDspV66Skel.so`
- `%QNN_SDK_ROOT%\examples\QNN\Saver\source\saver_output\params.bin`

To execute:

$ .\saver_output_QnnDsp.exe [--logging <verbose,debug,info,warn,error>] # optionally enable logging
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#### For HTP

Run on aarch64.

Please push below files to device:

- `%QNN_SDK_ROOT%\examples\QNN\Saver\build\Release\saver_output_QnnHtp.exe`
- `%QNN_SDK_ROOT%\lib\aarch64-windows-msvc\QnnHtp.dll`
- `%QNN_SDK_ROOT%\lib\aarch64-windows-msvc\QnnHtpPrepare.dll`
- `%QNN_SDK_ROOT%\lib\aarch64-windows-msvc\QnnHtp<Hexagon Architecture>Stub.dll`
- `%QNN_SDK_ROOT%\lib\hexagon-v68\unsigned\libQnnHtp<Hexagon Architecture>Skel.so`
- `%QNN_SDK_ROOT%\examples\QNN\Saver\source\saver_output\params.bin`

To execute:

$ .\saver_output_QnnHtp.exe [--logging <verbose,debug,info,warn,error>] # optionally enable logging
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Last Published: Oct 10, 2025

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