A Quarter of a Century of Neuromorphic Architectures on FPGAs -- an Overview

TL;DR

This paper reviews 25 years of digital neuromorphic architectures implemented on FPGAs, highlighting design choices, trends, and future directions in the field.

Contribution

It provides a comprehensive taxonomy of FPGA-based neuromorphic architectures, comparing their features, advantages, disadvantages, and future trends.

Findings

Digital NMAs show promise in accuracy and noise resilience.

Taxonomy highlights key architectural features and their trade-offs.

Identifies trends and future predictions in FPGA neuromorphic design.

Abstract

Neuromorphic computing is a relatively new discipline of computer science, where the principles of biological brain's computation and memory are used to create a new way of processing information, based on networks of spiking neurons. Those networks can be implemented as both analog and digital implementations, where for the latter, the Field Programmable Gate Arrays (FPGAs) are a frequent choice, due to their inherent flexibility, allowing the researchers to easily design hardware neuromorphic architecture (NMAs). Moreover, digital NMAs show good promise in simulating various spiking neural networks because of their inherent accuracy and resilience to noise, as opposed to analog implementations. This paper presents an overview of digital NMAs implemented on FPGAs, with a goal of providing useful references to various architectural design choices to the researchers interested in digital neuromorphic systems. We present a taxonomy of NMAs that highlights groups of distinct architectural features, their advantages and disadvantages and identify trends and predictions for the future of those architectures.