# Convert to QNN for Linux Host on HTP Backend Note This is **Part 3** of the Convert to QNN tutorial for Linux host machines. If you have not completed Part 2, please do so [here](https://docs.qualcomm.com/doc/80-63442-10/topic/tutorial_convert_execute_cnn_model.html). Warning HTP processors require quantized models instead of full precision models. If you do not have a quantized model, please follow Step 2 of the CNN to QNN tutorial to build one. ## Additional HTP Required Setup *Running the model on a target device’s HTP requires the generation of a \*\*serialized context\**.\* **On the Linux Host:** 1. Navigate to the directory where you built the model in the previous steps: cd /tmp/qnn_tmp Copy to clipboard 2. Users can set the custom options and different performance modes to HTP Backend through the backend config. Please refer to [QNN HTP Backend Extensions](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-10/htp_backend.html) for various options available in the config. 3. Refer to the example below for creating a backend config file for the QCS6490/QCM6490 target with mandatory options passed in: { "graphs": [ { "graph_names": [ "Inception_v3" ], "vtcm_mb": 2 } ], "devices": [ { "htp_arch": "v68" } ] } Copy to clipboard 4. The above config file with minimum parameters such as backend extensions config specified through JSON is given 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 5. To generate the context, update `` below with the config you wrote above, then run the command: "$QNN_SDK_ROOT/bin/${QNN_TARGET_ARCH}/qnn-context-binary-generator" \ --backend "${QNN_SDK_ROOT}/lib/${QNN_TARGET_ARCH}/libQnnHtp.so" \ --model "${QNN_MODEL_PATH}" \ --binary_file "${CONVERTED_MODEL_PATH##*/}.serialized" \ --config_file Copy to clipboard 6. This creates the serialized context at: - `${PWD}/output/${CONVERTED_MODEL_PATH##*/}.serialized.bin` ## Transferring over all relevant files 1. On the target device, open a terminal and make a destination folder by running: mount -o remount,rw / mkdir -p /data/local/tmp cd /data/local/tmp ln -s /etc/ /data/local/tmp chmod -R 777 /data/local/tmp mkdir -p /data/local/tmp/qnn_tutorial Copy to clipboard 2. Determine your target device’s SnapDragon architecture by looking up your chipset in the [Supported Snapdragon Devices](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-10/QNN_general_overview.html#supported-snapdragon-devices) table. 3. Update the “X” values below and run the commands to set `HTP_ARCH` to match the version number found in the above table. Only the 2 digits at the end should update, and they should have the same version. Ex. For “V68”, the proper value would be `hexagon-v68`. $HTP_VERSION="XX" $HTP_ARCH="hexagon-v${HTP_VERSION}" Copy to clipboard 4. Use `scp` to transfer `libQnnHtp.so` as well as other necessary executables from your host machine to `/data/local/tmp/qnn_tutorial` on the target device. scp "$QNN_SDK_ROOT/lib/${QNN_TARGET_ARCH}/libQnnHtp.so" "${TARGET_USER}@${TARGET_IP}:/data/local/tmp/qnn_tutorial" scp "${QNN_SDK_ROOT}/lib/${DSP_ARCH}/unsigned/*" "${TARGET_USER}@${TARGET_IP}:/data/local/tmp/qnn_tutorial" scp "${QNN_SDK_ROOT}/lib/${QNN_TARGET_ARCH}/libQnnHtpV${HTP_VERSION}Stub.so" "${TARGET_USER}@${TARGET_IP}:/data/local/tmp/qnn_tutorial" scp "${QNN_SDK_ROOT}/examples/Models/InceptionV3/output/Inception_v3.serialized.bin" "${TARGET_USER}@${TARGET_IP}:/data/local/tmp/qnn_tutorial" Copy to clipboard 5. Check the [Backend table](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-10/backend.html) to see if there are any other processor-specific executables needed for your target processor (`DSP`) and your target device’s architecture (`$QNN_TARGET_ARCH`). Use similar syntax above for `scp` to transfer any additional `.so` files listed **below** your selected target architecture in [this table](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-10/backend.html). **(There may be none!)** Note Ensure you transfer the `hexagon-v##` values (in addition to the other architecture files!) 6. Use `scp` to transfer the example built model. Update the `x64` folder below to the proper folder for your built model. The folder name depends on your host machine’s architecture. scp "${QNN_MODEL_PATH}" "${TARGET_USER}@${TARGET_IP}:/data/local/tmp/qnn_tutorial" Copy to clipboard 7. Transfer the input data, input list, and script from the QNN SDK examples folder into `/data/local/tmp/qnn_tutorial` on the target device using `scp` in a similar way: scp -r "${QNN_SDK_ROOT}/examples/Models/InceptionV3/data/cropped" "${TARGET_USER}@${TARGET_IP}:/data/local/tmp/qnn_tutorial" scp "${QNN_SDK_ROOT}/examples/Models/InceptionV3/data/target_raw_list.txt" "${TARGET_USER}@${TARGET_IP}:/data/local/tmp/qnn_tutorial" scp "${QNN_SDK_ROOT}/examples/Models/InceptionV3/data/imagenet_slim_labels" "${TARGET_USER}@${TARGET_IP}:/data/local/tmp/qnn_tutorial" scp "${QNN_SDK_ROOT}/examples/Models/InceptionV3/scripts/show_inceptionv3_classifications.py" "${TARGET_USER}@${TARGET_IP}:/data/local/tmp/qnn_tutorial" Copy to clipboard 8. Transfer `qnn-net-run` from `$QNN_SDK_ROOT/bin/$QNN_TARGET_ARCH/qnn-net-run` to `/data/local/tmp/qnn_tutorial` on the target device: scp "${QNN_SDK_ROOT}/bin/${QNN_TARGET_ARCH}/qnn-net-run" "${TARGET_USER}@${TARGET_IP}:/data/local/tmp/qnn_tutorial" Copy to clipboard 9. On the target device, set the environment variables: export LD_LIBRARY_PATH="/data/local/tmp/qnn_tutorial" export ADSP_LIBRARY_PATH="/data/local/tmp/qnn_tutorial" Copy to clipboard ## Doing inferences on the target device processor 1. Open a terminal instance on the target device. ssh "${TARGET_USER}@${TARGET_IP}" Copy to clipboard Note You will have to login with your target device’s login for that username. 2. On your target device, navigate to the directory containing the test files: cd /data/local/tmp/qnn_tutorial Copy to clipboard 3. Run the following command on the target device to execute an inference: ./qnn-net-run \ --backend "libQnnHtp.so" \ --input_list "target_raw_list.txt" \ --retrieve_context ".serialized.bin" \ --output_dir "./output" Copy to clipboard 4. Run the following script on the target device to view the classification results: Note You can alternatively copy the output folder back to your host machine with `scp` and run the following script there to avoid having to install python on your target device. python3 ".\show_inceptionv3_classifications.py" \ -i ".\cropped\raw_list.txt" \ -o "output" \ -l ".\imagenet_slim_labels.txt" Copy to clipboard 5. Verify that the classification results in `output` match the following: 1. `${QNN_SDK_ROOT}/examples/Models/InceptionV3/data/cropped/trash_bin.raw 0.777344 413 ashcan` 2. `${QNN_SDK_ROOT}/examples/Models/InceptionV3/data/cropped/chairs.raw 0.253906 832 studio couch` 3. `${QNN_SDK_ROOT}/examples/Models/InceptionV3/data/cropped/plastic_cup.raw 0.980469 648 measuring cup` 4. `${QNN_SDK_ROOT}/examples/Models/InceptionV3/data/cropped/notice_sign.raw 0.167969 459 brass` Last Published: Jun 04, 2026 [Previous Topic Convert to QNN for Linux Host on DSP Backend](https://docs.qualcomm.com/bundle/publicresource/80-63442-10/topics/dsp.md) [Next Topic Convert to QNN for Linux Host on LPAI Backend](https://docs.qualcomm.com/bundle/publicresource/80-63442-10/topics/lpai.md)