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I am facing an issue while trying to run an Android application Image Classification, Semantic Segmentation, Super Resolution using GPU and NPU on the Volterra Dev Kit 2023. Despite enabling the appropriate settings and integrating QnnDelegate for TensorFlow Lite, there is no evidence that the application is utilizing either the GPU or NPU.
Environment Details:
Device: Volterra Dev Kit 2023 (ARM64 Architecture)
OS: Windows 11 (ARM64 Version)
Android Subsystem: Windows Subsystem for Android (WSA)
TensorFlow Lite Version: 2.12.0
QnnDelegate Version: Latest (from Qualcomm SDK)
NDK Version: 23.2.8568313
CMake Version: 3.22.1
Steps to Reproduce:
Installed Windows Subsystem for Android (WSA) and enabled Developer Mode.
Configured ADB and successfully deployed the application.
Added TensorFlow Lite dependencies and QnnDelegate libraries in the Android project.
Used the following code to add QnnDelegate with GPU backend:
Verify whether the GPU and NPU on the Volterra Dev Kit 2023 are supported by the QnnDelegate.
Unfortunately, my colleagues and I here at the ai-hub-apps side don't know much about the Volterra Dev Kit and getting GPU/NPU to work in that context. You may want to reach out via the Qualcomm Discord (https://discord.gg/TzvP3JhfzX) to see if anyone in there has any experience with this.
Provide a method to log or trace backend usage (e.g., GPU/NPU).
Description:
I am facing an issue while trying to run an Android application Image Classification, Semantic Segmentation, Super Resolution using GPU and NPU on the Volterra Dev Kit 2023. Despite enabling the appropriate settings and integrating QnnDelegate for TensorFlow Lite, there is no evidence that the application is utilizing either the GPU or NPU.
Environment Details:
Steps to Reproduce:
Expected Behavior:
Actual Behavior:
Logs and Observations:
Attempts to Resolve:
libQnnCpu.so
,libQnnGpu.so
) are loaded.Request:
I would like assistance to:
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