Deploying the TensorFlow Lite for Microcontrollers program on QEMU
The skeleton of our Zephyr Project is ready, so we just need to finalize our application to classify our input test image.
In this recipe, we will see how to implement the tflite-micro application and run the model on the emulated Arm Cortex-M3-based microcontroller.
Getting ready
Most of the ingredients required for this recipe are related to tflite-micro and have already been covered in earlier chapters, such as Chapter 3, Building a Weather Station with TensorFlow Lite for Microcontrollers. Nevertheless, there is one useful feature of this framework that has not been introduced yet, but it can come in handy when we want to reduce the program memory usage of our application drastically. This feature is the tflite::MicroMutableOpResolver, which enables the compilation of a subset of all operators available in tflite-micro.
As shown in the following ML model example, the model is composed of a series...
