Design and implementation of spiking neural networks on FPGA for event-based spatio-temporal applications.

Abstract

Inspired by the intricacies of real biological neural systems, Spiking Neural Networks (SNNs) represent an advanced type of artificia lneura lnetwork .SNN soperat ewith discrete spikes, closely mimicking the way neurons communicate in the human brain. This unique method of information processing not only enhances the computational efficien cy ofSN Nsb utal soope ns upn ewpossibiliti esf ordevelopi nglow-pow erneural network systems. In this work, we proposed a generic hardware design of an SNN based on Field-Programmable Gate Arrays (FPGA). The proposed design was implemented and tested with the event-based benchmark dataset “Neuromorphic-MNIST” and managed to achieve a low power consumption and latency, while requiring very minimal hardware resources, all this for an evaluated accuracy.