Towards scalable memristive hardware for spiking neural networks
Peng Chen, Bihua Zhang, Enhui He, Yu Xiao, Fenghao Liu, Peng Lin, Zhongrui Wang, Gang Pan
Abstract
Spiking neural networks (SNNs) represent a promising frontier in artificial intelligence (AI), offering event-driven, energy-efficient computation that mimics rich neural dynamics in the brain. However, running large-scale SNNs on mainstream computing hardware faces significant challenges to efficiently emulate these dynamical processes using synchronized and logical chips. Memristor based systems have recently demonstrated great potential for AI acceleration, sparking speculations and explorations of using these emerging devices for SNN tasks. This paper reviews the promises and challenges of memristive devices in SNN implementations, and our discussions are focused on the scaling and integration of neuronal and synaptic devices. We survey recent progress in device and circuit development, discuss possible pathways for chip-level integration, and finally probe into hardware-oriented algorithm designs. This review offers a system-level perspective on implementing scalable memristor based SNN platforms.