Exploring Spike-Based Learning for Neuromorphic Computing: Prospects and Perspectives

Abstract

Spiking neural networks (SNNs) operating with sparse binary signals (spikes) implemented on event-driven hardware can potentially be more energy -efficient than traditional artificial neural networks (ANNs). However, SNNs perform computations over time, and the neuron activation function does not have a well-defined derivative leading to unique training challenges. In this paper, we discuss the various spike representations and training mechanisms for deep SNN s. Additionally, we review applications that go beyond classification, like gesture recognition, motion estimation, and sequential learning. The unique features of SNNs, such as high activation sparsity and spike-based computations, can be leveraged in hardware implementations for energy-efficient processing. To that effect, we discuss various SNN implementations, both using digital ASICs as well as analog in-memory computing primitives. Finally, we present an outlook on future applications and open research areas for both SNN algorithms and hardware implementations.