How to run deep learning model on microcontroller with CMSIS-NN (Part 1)

TL;DR You will learn how to run the CIFAR10 image classification model on an ARM microcontroller like the one on STM32F4 Discovery board or similar.

Why run deep learning model on a microcontroller?

If you have played with Arduino before, it’s easy to have the impression that they are small little chips with limited computing and memory resources, but exceptional when comes to collect data from various sensors or control a servo on a robot hand.

Many microcontrollers either run on a real-time operating system like the FreeRTOS or its bare metal without an OS. Both ways make them quite stable and responsive, especially in mission-critical situations.

However, as more and more data collected with its sensors, the two most common type of data sound and image required a significant amount of computing resource to process to generate useful results. This task is normally accomplished by asking the microcontroller upload data to a network-connected server, and the server sends the processed results back to the edge, a microcontroller will then conduct specific behaviors, like response with greetings and switch on a light.