# UDO Tutorial Overview This tutorial describes the steps needed to create a UDO package and execute the Inception V3 model using the package. The Softmax operation has been chosen in this tutorial to demonstrate the implementation of a UDO with Qualcomm® Neural Processing SDK. The Qualcomm® Neural Processing SDK provides the resources for this example under - $SNPE\_ROOT/examples/SNPE/NativeCpp/UdoExample/Softmax Information on UDO in general is available at UDO Overview. Information on running the Inception V3 network without UDO is available at Inception V3 Tutorial. Prerequisites The following tutorial assumes that general Qualcomm (R) Neural Processing SDK setup has been followed to support SDK environment, TensorFlow environment, and desired platform dependencies. Please ensure that your hexagon sdk is named in the format “hexagon-sdk-<version name>” and ensure that the hexagon sdk has the path for the tools directory in format of “hexagon-sdk-<version name>/tools/HEXAGON\_Tools/<tools name>/” Additionally, we need an extracted Qualcomm® AI Direct SDK (no need of Qualcomm® AI Direct SDK setup) for generating the skeleton code and building the libraries. For Qualcomm® AI Direct SDK details, refer to the Qualcomm® AI Direct SDK documentation at `$QNN_SDK_ROOT/docs/QNN/index.html` page, where `QNN_SDK_ROOT` is the location of the Qualcomm® AI Direct SDK installation. Set the `$QNN_SDK_ROOT` to the unzipped Qualcomm® AI Direct SDK location. This has to be performed after running the envsetup.sh script mentioned in SNPE Setup. The steps listed in this tutorial use the Tensorflow model in the form of inception\_v3\_2016\_08\_28\_frozen.pb. For details on acquiring the Inception V3 model visit Tutorials Setup. Introduction Here are the steps to develop and run a UDO 1. Package Generation 2. Framework Model Conversion to a DLC 3. Package Implementation 4. Package Compilation 5. Model Execution Steps 1-4 are run offline on the x86 host and are necessary for execution in step 5. Step 5 provides information on execution using the Qualcomm® Neural Processing SDK command-line executable **snpe-net-run**. Optionally, the user can perform steps 1-4 automatically using the provided setup script. Make sure to run following commands for setting up the tensorflow paths and hexagon path export TENSORFLOW_HOME= export TENSORFLOW_ROOT=$TENSORFLOW_HOME/focal/py3 export PYTHONPATH=$TENSORFLOW_ROOT/distribute:$TENSORFLOW_ROOT/dependencies/python:$PYTHONPATH # hexagon sdk and tools version depend on dsp aarch, below version is for v68 aarch export HEXAGON_SDK_ROOT= export HEXAGON_TOOLS_ROOT=$HEXAGON_SDK_ROOT/tools/HEXAGON_Tools/8.4.09 Copy to clipboard Step 1: Package Generation Generating the SoftmaxUdoPackage requires the **snpe-udo-package-generator** tool and the provided UDO plugin: Softmax\_Htp.json. The plugin is located under $SNPE\_ROOT/examples/SNPE/NativeCpp/UdoExample/Softmax/config. More information about creating a UDO plugin can be found here. Generate the SoftmaxUdoPackage using the following: export SNPE_UDO_ROOT=$SNPE_ROOT/share/SNPE/SnpeUdo export QNN_SDK_ROOT= snpe-udo-package-generator -p $SNPE_ROOT/examples/SNPE/NativeCpp/UdoExample/Softmax/config/Softmax_Htp.json -o $SNPE_ROOT/examples/Models/InceptionV3/ Copy to clipboard This command creates the Softmax based package at $SNPE\_ROOT/examples/Models/InceptionV3/SoftmaxUdoPackage. For more information on the snpe-udo-package-generator tool visit here. Step 2: Framework model Conversion to a DLC Converting the Tensorflow Inception V3 model to DLC requires the snpe-tensorflow-to-dlc tool. The snpe-tensorflow-to-dlc tool consumes the same Softmax\_Htp.json used in package generation via the –udo command line option. In this step, <INCEPTION\_V3\_PATH> refers to the path to the inception\_v3 pb file. For example, after running the setup\_inceptionv3\_snpe.py script <INCEPTION\_V3\_PATH> is $SNPE\_ROOT/examples/Models/InceptionV3/tensorflow. Convert Inception V3 with the following: snpe-tensorflow-to-dlc --input_network /inception_v3_2016_08_28_frozen.pb --input_dim 'input' 1,299,299,3 --out_node InceptionV3/Predictions/Reshape_1 --output_path $SNPE_ROOT/examples/Models/InceptionV3/dlc/inception_v3_udo.dlc --udo $SNPE_ROOT/examples/SNPE/NativeCpp/UdoExample/Softmax/config/Softmax_Htp.json Copy to clipboard This will generate a DLC named inception\_v3\_udo.dlc containing the Softmax as UDO at $SNPE\_ROOT/examples/Models/InceptionV3/dlc. Step 3: Package Implementations The generated package creates the skeleton of the operation implementation, which must be filled by the user to create a functional UDO. The rest of the code scaffolding for compatibility with Qualcomm® Neural Processing SDK is provided by the **snpe-udo-package-generator**. The UDO implementations for this tutorial are provided under $SNPE\_ROOT/examples/SNPE/NativeCpp/UdoExample/Softmax/src. **CPU Implementations (Android and x86)** The file in the package that needs to be implemented for CPU is - SoftmaxUdoPackage/jni/src/CPU/src/ops/Softmax.cpp The provided example implementation is present at the location - $SNPE\_ROOT/examples/SNPE/NativeCpp/UdoExample/Softmax/src/CPU/Softmax.cpp Copy the provided implementation to the package: cp -f $SNPE_ROOT/examples/SNPE/NativeCpp/UdoExample/Softmax/src/CPU/Softmax.cpp $SNPE_ROOT/examples/Models/InceptionV3/SoftmaxUdoPackage/jni/src/CPU/src/ops/ Copy to clipboard Optionally, the user can provide their own implementations in the package. **GPU Implementations (Android)** The file in the package that needs to be implemented for GPU is - SoftmaxUdoPackage/jni/src/GPU/src/ops/Softmax.cpp The provided example implementation is present at the location - $SNPE\_ROOT/examples/SNPE/NativeCpp/UdoExample/Softmax/src/GPU/Softmax.cpp Copy the provided implementation to the package: cp -f $SNPE_ROOT/examples/SNPE/NativeCpp/UdoExample/Softmax/src/GPU/Softmax.cpp $SNPE_ROOT/examples/Models/InceptionV3/SoftmaxUdoPackage/jni/src/GPU/src/ops/ Copy to clipboard Optionally, the user can provide their own implementations in the package. **DSP Implementations for V65 and V66** Similar to all other Qualcomm® Neural Processing SDK runtimes, a registration library and an implementation library are required to run inference of a network with UDO layers on Qualcomm® Neural Processing SDK DSP. The registration library will run on CPU, and specifies the DSP implementation library of the UDO. Refer Implementing a UDO for DSP V65 and V66 for more information on implementing UDO for DSP V65 and V66 runtimes. The file in the package that need to be implemented for DSP V65 and V66 are - SoftmaxUdoPackage/jni/src/DSP/Softmax.cpp The provided example implementation is present at the location - $SNPE\_ROOT/examples/SNPE/NativeCpp/UdoExample/Softmax/src/DSP/Softmax.cpp Copy the provided implementations to the package: cp -f $SNPE_ROOT/examples/SNPE/NativeCpp/UdoExample/Softmax/src/DSP/Softmax.cpp $SNPE_ROOT/examples/Models/InceptionV3/SoftmaxUdoPackage/jni/src/DSP/src/ops Copy to clipboard Optionally, the user can provide their own implementations in the package. **DSP Implementations for V68 and later** Refer Implementing a UDO for DSP V68 or later for more information on implementing UDO for DSP V68 or later runtimes. The directory paths and locations in this example are specific to DSP V68. For later runtimes, please replace **DSP\_V68** with the corresponding DSP architecture (for example, **DSP\_V69**) in the paths. The file in the package that needs to be implemented for DSP V68 and later is - SoftmaxUdoPackage/jni/src/DSP\_V68/src/ops/Softmax.cpp The provided example implementation is present at the location - $SNPE\_ROOT/examples/SNPE/NativeCpp/UdoExample/Softmax/src/HTP/Softmax.cpp Copy the provided implementations to the package: cp -f $SNPE_ROOT/examples/SNPE/NativeCpp/UdoExample/Softmax/src/HTP/Softmax.cpp $SNPE_ROOT/examples/Models/InceptionV3/SoftmaxUdoPackage/jni/src/DSP_V68/src/ops Copy to clipboard Optionally, the user can provide their own implementations in the package. Step 4: Package Compilation **x86 Host Compilation** Compiling on x86 host uses the make build system. Compile the CPU implementations with the following: cd $SNPE_ROOT/examples/Models/InceptionV3/SoftmaxUdoPackage make cpu_x86 Copy to clipboard The expected artifacts after compiling for CPU on x86 host are - SoftmaxUdoPackage/libs/x86-64\_linux\_clang/libUdoSoftmaxUdoPackageImplCpu.so - SoftmaxUdoPackage/libs/x86-64\_linux\_clang/libUdoSoftmaxUdoPackageReg.so **Android CPU Runtime Compilation** Compilation for the CPU runtime on Android uses Android NDK. The ANDROID\_NDK\_ROOT environment variable must be set to the directory containing ndk-build in order to compile the package. export ANDROID_NDK_ROOT= Copy to clipboard It is suggested to add ANDROID\_NDK\_ROOT to the PATH environment variable to access ndk-build. export PATH=$ANDROID_NDK_ROOT:$PATH Copy to clipboard Once the ANDROID\_NDK\_ROOT is part of PATH, compile the package for Android CPU target: cd $SNPE_ROOT/examples/Models/InceptionV3/SoftmaxUdoPackage make cpu_android Copy to clipboard The expected artifacts after compiling for Android CPU are - SoftmaxUdoPackage/libs/arm64-v8a/libUdoSoftmaxUdoPackageImplCpu.so - SoftmaxUdoPackage/libs/arm64-v8a/libUdoSoftmaxUdoPackageReg.so - SoftmaxUdoPackage/libs/arm64-v8a/libc++\_shared.so **Hexagon DSP Runtime Compilation** Compilation for the DSP runtime makes use of the make system. In order to build the implementation libraries for DSP V65 and V66 runtimes, Hexagon-SDK needs to be installed and set up. For details, follow the setup instructions on `$HEXAGON_SDK_ROOT/docs/readme.html` page, where `HEXAGON_SDK_ROOT` is the location of your Hexagon-SDK installation. Information for compiling a UDO for DSP is available at Compiling UDO for DSP. Setup Script The Qualcomm® Neural Processing SDK provides an option to automatically perform steps of DLC conversion, package generation, package implementation, and package compilation for UDO as outlined in steps 1-4 above. The option is an extension of the Inception V3 setup script. To enable Inception V3 setup for UDO, run the script with the **–udo** or **-u** option. usage: $SNPE_ROOT/models/examples/Models/InceptionV3/scripts/setup_inceptionv3_snpe.py [-h] -a ASSETS_DIR [-d] [-r RUNTIME] [-u] [-l [HTP_SOC]] Prepares the InceptionV3 assets for tutorial examples. required arguments: -a ASSETS_DIR, --assets_dir ASSETS_DIR directory containing the InceptionV3 assets optional arguments: -d, --download Download InceptionV3 assets to InceptionV3 example directory -r RUNTIME, --runtime RUNTIME Choose a runtime to set up tutorial for. Choices: cpu, gpu, dsp, aip, all. 'all' option is only supported with --udo flag -u, --udo Generate and compile a user-defined operation package to be used with InceptionV3. Softmax is simulated as a UDO for this script. -l [HTP_SOC], --htp_soc [HTP_SOC] Specify SOC target for generating HTP Offline Cache. For example: "--htp_soc sm8450" for Snapdragon 8 Gen 1, default value is sm8750. Copy to clipboard The –udo extension is compatible with options normally used by the setup\_inceptionv3\_snpe.py script. When the –udo option is enabled, the -r or –runtime option controls the runtime for the package implementation and compilation. Additionally, the –udo option supports use of an ‘all’ runtime option to create and compile the SoftmaxUdoPackage for the CPU, GPU, and DSP/AIP runtimes. Selecting the ‘aip’ or ‘dsp’ runtime options additionally compiles x86 libraries in order to quantize the model. Selecting the ‘cpu’ runtime option compiles for both x86 and Android targets. Compilation for Android target will be skipped if ANDROID\_NDK\_ROOT is not set. If no runtime option is provided, the package is compiled for the CPU runtime. The command to use the setup script for UDO is: python3 $SNPE_ROOT/examples/Models/InceptionV3/scripts/setup_inceptionv3_snpe.py -a ~/tmpdir -d -u -r Copy to clipboard For instance, to create and compile a package containing the libraries for all runtimes run: python3 $SNPE_ROOT/examples/Models/InceptionV3/scripts/setup_inceptionv3_snpe.py -a ~/tmpdir -d -u -r all Copy to clipboard For DSP\_V68 or later, use the -l optional argument of the setup script to specify the htp soc. Or follow the steps here. This will populate the artifacts in Step 4. Model Execution **Execution using snpe-net-run** Executing Inception V3 with UDO is largely the same as use of snpe-net-run without UDO. The Qualcomm® Neural Processing SDK provides Linux and Android binaries of **snpe-net-run** under - $SNPE\_ROOT/bin/x86\_64-linux-clang - $SNPE\_ROOT/bin/aarch64-android - $SNPE\_ROOT/bin/aarch64-oe-linux-gcc8.2 - $SNPE\_ROOT/bin/aarch64-oe-linux-gcc9.3 - $SNPE\_ROOT/bin/aarch64-ubuntu-gcc9.4 - $SNPE\_ROOT/bin/aarch64-oe-linux-gcc11.2 For UDO, snpe-net-run consumes the registration library through the –udo\_package\_path option. LD\_LIBRARY\_PATH must also be updated to include the runtime-specific artifacts generated from package compilation. **x86 Host Execution** To execute the network on x86 host, run: cd $SNPE_ROOT/examples/Models/InceptionV3 export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$SNPE_ROOT/examples/Models/InceptionV3/SoftmaxUdoPackage/libs/x86-64_linux_clang/ snpe-net-run --container dlc/inception_v3_udo.dlc --input_list data/cropped/raw_list.txt --udo_package_path SoftmaxUdoPackage/libs/x86-64_linux_clang/libUdoSoftmaxUdoPackageReg.so Copy to clipboard **Android Target Execution** The tutorial for execution on Android targets will use the arm64-v8a architecture. This portion of the tutorial is generic to all runtimes (CPU, GPU, DSP). Set SNPE\_TARGET\_DSPARCH to the DSP architecture of the target Android device. # architecture: arm64-v8a - compiler: clang - STL: libc++ export SNPE_TARGET_ARCH=aarch64-android export SNPE_TARGET_DSPARCH=hexagon-v68 Copy to clipboard Then, push Qualcomm® Neural Processing SDK binaries and libraries to the target device: adb shell "mkdir -p /data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/bin" adb shell "mkdir -p /data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/lib" adb push $SNPE_ROOT/lib/$SNPE_TARGET_ARCH/*.so \ /data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/lib adb push $SNPE_ROOT/bin/$SNPE_TARGET_ARCH/snpe-net-run \ /data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/bin Copy to clipboard Next, update environment variables on the target device to include the Qualcomm® Neural Processing SDK libraries and binaries: adb shell export SNPE_TARGET_ARCH=aarch64-android export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/lib export PATH=$PATH:/data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/bin Copy to clipboard Lastly, push the Inception V3 UDO model and input data to the device: cd $SNPE_ROOT/examples/Models/InceptionV3 mkdir data/rawfiles && cp data/cropped/*.raw data/rawfiles/ adb shell "mkdir -p /data/local/tmp/inception_v3_udo" adb push data/rawfiles /data/local/tmp/inception_v3_udo/cropped adb push data/target_raw_list.txt /data/local/tmp/inception_v3_udo adb push dlc/inception_v3_udo.dlc /data/local/tmp/inception_v3_udo rm -rf data/rawfiles Copy to clipboard **Android CPU Execution** Once the model and data have been placed on the device, place the UDO libraries on the device: cd $SNPE_ROOT/examples/Models/InceptionV3 adb shell "mkdir -p /data/local/tmp/inception_v3_udo/cpu" adb push SoftmaxUdoPackage/libs/arm64-v8a/libUdoSoftmaxUdoPackageImplCpu.so /data/local/tmp/inception_v3_udo/cpu adb push SoftmaxUdoPackage/libs/arm64-v8a/libUdoSoftmaxUdoPackageReg.so /data/local/tmp/inception_v3_udo/cpu adb push SoftmaxUdoPackage/libs/arm64-v8a/libc++_shared.so /data/local/tmp/inception_v3_udo/cpu Copy to clipboard Now set required environment variables and run snpe-net-run on device: adb shell cd /data/local/tmp/inception_v3_udo/ export SNPE_TARGET_ARCH=aarch64-android export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/lib export PATH=$PATH:/data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/bin export LD_LIBRARY_PATH=/data/local/tmp/inception_v3_udo/cpu/:$LD_LIBRARY_PATH snpe-net-run --container inception_v3_udo.dlc --input_list target_raw_list.txt --udo_package_path cpu/libUdoSoftmaxUdoPackageReg.so Copy to clipboard **Android GPU Execution** The procedure for execution using the GPU runtime is largely similar to execution procedure for CPU. First, use the following to place the UDO libraries on device: cd $SNPE_ROOT/examples/Models/InceptionV3 adb shell "mkdir -p /data/local/tmp/inception_v3_udo/gpu" adb push SoftmaxUdoPackage/libs/arm64-v8a/libUdoSoftmaxUdoPackageImplGpu.so /data/local/tmp/inception_v3_udo/gpu adb push SoftmaxUdoPackage/libs/arm64-v8a/libUdoSoftmaxUdoPackageReg.so /data/local/tmp/inception_v3_udo/gpu adb push SoftmaxUdoPackage/libs/arm64-v8a/libc++_shared.so /data/local/tmp/inception_v3_udo/gpu Copy to clipboard Now set required environment variables and run snpe-net-run to device: adb shell cd /data/local/tmp/inception_v3_udo/ export SNPE_TARGET_ARCH=aarch64-android export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/lib export PATH=$PATH:/data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/bin export LD_LIBRARY_PATH=/data/local/tmp/inception_v3_udo/gpu/:$LD_LIBRARY_PATH snpe-net-run --container inception_v3_udo.dlc --input_list target_raw_list.txt --udo_package_path gpu/libUdoSoftmaxUdoPackageReg.so --use_gpu Copy to clipboard **Hexagon DSP Execution** The procedure for execution on device for DSP is largely the same as CPU and GPU. However, the DSP runtime requires quantized network parameters. While DSP allows unquantized DLCs, it is generally recommended to quantize DLCs for improved performance. The tutorial will use a quantized DLC as an illustrative example. Quantizing the DLC requires the **snpe-dlc-quantize** tool. To quantize the DLC for use on DSP: cd $SNPE_ROOT/examples/Models/InceptionV3/ snpe-dlc-quantize --input_dlc dlc/inception_v3_udo.dlc --input_list data/cropped/raw_list.txt --udo_package_path SoftmaxUdoPackage/libs/x86-64_linux_clang/libUdoSoftmaxUdoPackageReg.so --output_dlc dlc/inception_v3_udo_quantized.dlc Copy to clipboard For more information on **snpe-dlc-quantize** visit quantization. For information on UDO-specific quantization visit Quantizing a DLC with UDO. For information on DSP/AIP runtime visit DSP Runtime or AIP Runtime. Now push the quantized model to device: adb push dlc/inception_v3_udo_quantized.dlc /data/local/tmp/inception_v3_udo Copy to clipboard **Note:** Please refer to UDO DSP tutorial for Quantized DLC for executing on the DSP runtime using quantized dlc. Before executing on the DSP, push the Qualcomm® Neural Processing SDK libraries for DSP to device: adb shell "mkdir -p /data/local/tmp/snpeexample/dsp/lib" adb push $SNPE_ROOT/lib/$SNPE_TARGET_DSPARCH/unsigned/*.so /data/local/tmp/snpeexample/dsp/lib Copy to clipboard Now push DSP-specific UDO libraries to device. Depending on DSP architecture specified in the config, **dsp\_v68** directory can be **dsp\_v60** or **dsp** (with older Qualcomm® Neural Processing SDK). cd $SNPE_ROOT/examples/Models/InceptionV3 adb shell "mkdir -p /data/local/tmp/inception_v3_udo/dsp" adb push SoftmaxUdoPackage/libs/dsp_v68/*.so /data/local/tmp/inception_v3_udo/dsp adb push SoftmaxUdoPackage/libs/arm64-v8a/libUdoSoftmaxUdoPackageReg.so /data/local/tmp/inception_v3_udo/dsp # Pushes reg lib adb push SoftmaxUdoPackage/libs/arm64-v8a/libc++_shared.so /data/local/tmp/inception_v3_udo/dsp adb push SoftmaxUdoPackage/libs/arm64-v8a/libUdoSoftmaxUdoPackageImplDsp_AltPrep.so /data/local/tmp/inception_v3_udo/dsp #If online prepare is used Copy to clipboard Then set required environment variables and run snpe-net-run on device: adb shell cd /data/local/tmp/inception_v3_udo/ export SNPE_TARGET_ARCH=aarch64-android export LD_LIBRARY_PATH=/data/local/tmp/inception_v3_udo/dsp:/data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/lib export PATH=$PATH:/data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/bin export ADSP_LIBRARY_PATH="/data/local/tmp/inception_v3_udo/dsp/;/data/local/tmp/snpeexample/dsp/lib;/system/lib/rfsa/adsp;/system/vendor/lib/rfsa/adsp;/dsp" snpe-net-run --container inception_v3_udo_quantized.dlc --input_list target_raw_list.txt --udo_package_path dsp/libUdoSoftmaxUdoPackageReg.so --use_dsp Copy to clipboard **AIP Execution** Because UDOs are not supported on the HTA hardware, executing on the AIP runtime defaults to the DSP UDO implementations. HTA hardware runs exclusively on quantized models and therefore as with the DSP runtime, a quantized model will be used. The command to quantize the DLC for AIP is: cd $SNPE_ROOT/examples/Models/InceptionV3/ snpe-dlc-quantize --input_dlc dlc/inception_v3_udo.dlc --input_list data/cropped/raw_list.txt --udo_package_path SoftmaxUdoPackage/libs/x86-64_linux_clang/libUdoSoftmaxUdoPackageReg.so --output_dlc dlc/inception_v3_udo_quantized.dlc --enable_hta Copy to clipboard Now push the quantized model to device: adb push dlc/inception_v3_udo_quantized.dlc /data/local/tmp/inception_v3_udo Copy to clipboard Before executing using the AIP runtime, push the Qualcomm® Neural Processing SDK libraries for DSP to device with these commands: adb shell "mkdir -p /data/local/tmp/snpeexample/dsp/lib" adb push $SNPE_ROOT/lib/$SNPE_TARGET_DSPARCH/unsigned/*.so /data/local/tmp/snpeexample/dsp/lib Copy to clipboard Now push DSP-specific UDO libraries to device. Depending on DSP architecture specified in the config, **dsp\_v68** directory can be **dsp\_v60** or **dsp** (with older Qualcomm® Neural Processing SDK). cd $SNPE_ROOT/examples/Models/InceptionV3 adb shell "mkdir -p /data/local/tmp/inception_v3_udo/dsp" adb push SoftmaxUdoPackage/libs/dsp_v68/*.so /data/local/tmp/inception_v3_udo/dsp adb push SoftmaxUdoPackage/libs/arm64-v8a/libUdoSoftmaxUdoPackageReg.so /data/local/tmp/inception_v3_udo/dsp # Pushes reg lib adb push SoftmaxUdoPackage/libs/arm64-v8a/libc++_shared.so /data/local/tmp/inception_v3_udo/dsp Copy to clipboard Then set required environment variables and run snpe-net-run on device: adb shell cd /data/local/tmp/inception_v3_udo/ export SNPE_TARGET_ARCH=aarch64-android export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/lib export PATH=$PATH:/data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/bin export LD_LIBRARY_PATH=/data/local/tmp/inception_v3_udo/dsp/:$LD_LIBRARY_PATH export ADSP_LIBRARY_PATH="/data/local/tmp/inception_v3_udo/dsp/;/data/local/tmp/snpeexample/dsp/lib;/system/lib/rfsa/adsp;/system/vendor/lib/rfsa/adsp;/dsp" snpe-net-run --container inception_v3_udo_quantized.dlc --input_list target_raw_list.txt --udo_package_path dsp/libUdoSoftmaxUdoPackageReg.so --use_aip Copy to clipboard **Integration with Android APK** This portion of the tutorial outlines how to integrate Qualcomm® Neural Processing SDK UDO libraries and Java API for package registration into an Android application. Generally, for native shared libraries to be discoverable by the application they must be placed in the project under /app/src/main/jniLibs/ Copy to clipboard Once the libraries are accessible by the application, the registration library can be registered using the provided Java API. This process will be replicated with the example Image Classifiers application. The following assumes that the rest of the example application setup has been followed. The tutorial will issue instructions for platforms with arm64-v8a ABI. First, create the neccessary directories to contain the UDO libraries. The following steps will populate all runtime implementation libraries. mkdir app/src/main/jniLibs/ cp -a $SNPE_ROOT/examples/Models/InceptionV3/SoftmaxUdoPackage/libs/arm64-v8a/ app/src/main/jniLibs/ Copy to clipboard If DSP is to be used as the runtime, copy the implementation library with the following: cp $SNPE_ROOT/examples/Models/InceptionV3/SoftmaxUdoPackage/libs/dsp_v68/*.so app/src/main/jniLibs/arm64-v8a/ Copy to clipboard If not already done, running **setup\_inceptionv3.sh** will add the Inception V3 model enabled with UDO and image data to the project. bash ./setup_inceptionv3.sh Copy to clipboard Now the Java API can be registered. Include the Qualcomm® Neural Processing SDK AAR which includes necessary native binaries export SNPE_AAR=snpe-release.aar cp $SNPE_ROOT/lib/android/${SNPE_AAR} app/libs/${SNPE_AAR} Copy to clipboard Now the APK can be built and exercised # exporting gradle user_home is optional but suggested if USER_HOME storage is low export GRADLE_USER_HOME= export ANDROID_SDK_ROOT= ./gradlew assembleDebug Copy to clipboard For more information, visit Android Sample App. Last Published: May 06, 2026 Previous Topic Windows Tutorial Next Topic UDO DSP tutorial for Quantized DLC