Real-time Neural Networks Implementation Proposal for Microcontrollers

TL;DR

This paper proposes a modular, matrix-based implementation of a neural network on microcontrollers, demonstrating its viability for real-time applications in IoT and M2M by analyzing processing time and training accuracy.

Contribution

It introduces a novel strategy for implementing a full MLP neural network, including training, on low-power microcontrollers for real-time use.

Findings

Processing time scales linearly with hyperparameters

Implementation achieves acceptable training and classification accuracy

Platform suitable for real-time ANN applications in IoT and M2M

Abstract

The adoption of intelligent systems with Artificial Neural Networks (ANNs) embedded in hardware for real-time applications currently faces a growing demand in fields like the Internet of Things (IoT) and Machine to Machine (M2M). However, the application of ANNs in this type of system poses a significant challenge due to the high computational power required to process its basic operations. This paper aims to show an implementation strategy of a Multilayer Perceptron (MLP) type neural network, in a microcontroller (a low-cost, low-power platform). A modular matrix-based MLP with the full classification process was implemented, and also the backpropagation training in the microcontroller. The testing and validation were performed through Hardware in the Loop (HIL) of the Mean Squared Error (MSE) of the training process, classification result, and the processing time of each implementation module. The results revealed a linear relationship between the values of the hyperparameters and the processing time required for classification, also the processing time concurs with the required time for many applications on the fields mentioned above. These findings show that this implementation strategy and this platform can be applied successfully on real-time applications that require the capabilities of ANNs.