MicroPython or Arduino C for ESP32 Efficiency for Neural Network Edge Devices
In the last few years, microcontrollers used for IoT devices became more and more powerful, and many authors have started to use them in machine learning systems. Most of the authors used them just for data collecting for ML algorithms in the clouds, but some of them implemented ML algorithms on the microcontrollers. The goal of this paper is to analyses the neural networks data propagation speed of one popular SoC (Espressif System company ESP32) with simple neural networks implementation with two different development environment, Arduino IDE and MycroPython. Neural networks with one hidden layer are used with a different number of neurons. This SoC is analysed because some companies started to produce them with UXGA (Ultra Extended Graphics Array) camera implemented and it can be used to distribute computing load from central ML servers.