Bureaucrats, dave_user, Administrators
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→Version 2
The version 1 application was then modified to accelerate the inference using the NPU (ML module) of the [https://www.nxp.com/products/processors-and-microcontrollers/arm-processors/i-mx-applications-processors/i-mx-8-processors/i-mx-8m-plus-arm-cortex-a53-machine-learning-vision-multimedia-and-industrial-iot:IMX8MPLUS i.MX8M Plus] SoC. This is possible because ''the TensorFlow Lite library uses the Android NN API driver implementation from the GPU/ML module driver for running inference using the GPU/ML module''.
Neither the floating-point nor the half-quantized models work with the NPU, however. Moreover, ''the GPU/ML module driver does not support per-channel quantization yet. Therefore post-training quantization of models with TensorFlow v2 cannot be used if the model is supposed to run on the GPU/ML module (inference on CPU does not have this limitation). TensorFlow v1 quantization-aware training and model conversion is recommended in this case''. Therefore, only the fully-quantized model was tested with this version of the application.
=== Version 3 ===
