Embedded Artificial Neural Networks Optimized for Low-cost and Low-Size-Memory Devices
MLP, AI, 8-bits, microcontroller, artificial neural networks, embedded systems.
Artificial Neural Networks (ANNs) are bio-inspired systems with a high level of parallelization and almost infinite applications. However, due to the associated high computational power requirements, most application demands powerful processing characteristics and consequently, high-costs and not-so-small form-factors. This work presents an implementation of a Multilayer Perceptrons (MLPs) for 8-bit microcontrollers in two different scenarios, embedded training, and inference. Analysis of training convergence, inference time duration, and program code occupation into the internal memories and a technique to optimize this implementation to fit bigger MLP architectures. The aim of this work is to provide an overview of the feasibility of ANNs on these low-cost, low-size-memory devices, known as microcontrollers