Sparsity-Aware Hardware-Software Co-Design of Spiking Neural Networks: An Overview

TL;DR

This paper reviews the hardware-software co-design strategies for sparse Spiking Neural Networks, emphasizing how sparsity, neuron models, and training influence efficiency in neuromorphic systems.

Contribution

It provides an overview of how sparsity representation, hardware architectures, and training techniques can be integrated for efficient SNN implementations.

Findings

Analyzes static and dynamic sparsity impacts

Discusses neuron models and encoding schemes

Highlights the importance of hardware adaptability

Abstract

Spiking Neural Networks (SNNs) are inspired by the sparse and event-driven nature of biological neural processing, and offer the potential for ultra-low-power artificial intelligence. However, realizing their efficiency benefits requires specialized hardware and a co-design approach that effectively leverages sparsity. We explore the hardware-software co-design of sparse SNNs, examining how sparsity representation, hardware architectures, and training techniques influence hardware efficiency. We analyze the impact of static and dynamic sparsity, discuss the implications of different neuron models and encoding schemes, and investigate the need for adaptability in hardware designs. Our work aims to illuminate the path towards embedded neuromorphic systems that fully exploit the computational advantages of sparse SNNs.