Accelerating Spiking Neural Networks using Memristive Crossbar Arrays

Thomas Bohnstingl*, Angeliki Pantazi, Evangelos Eleftheriou

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

Biologically-inspiredspikingneuralnetworks(SNNs) hold great promise to perform demanding tasks in an energy and area-efficient manner. Memristive devices organized in a crossbar array can be used to accelerate operations of artificial neural networks (ANNs) while circumventing limitations of traditional computing paradigms. Recent advances have led to the development of neuromorphic accelerators that employ phase-change memory (PCM) devices. We propose an approach to fully unravel the potential of such systems for SNNs by integrating entire layers, including synaptic weights as well as neuronal states, into crossbar arrays. However,the key challenges of such realizations originate from the intrinsic imperfections of the PCM devices that limit their effective precision. Thus, we investigated the impact of these limitations on the performance of SNNs and demonstrate that the synaptic weight and neuronal state realization using 4-bitprecision provides a robust network performance. Moreover, we evaluated the scheme for a multi-layer SNN realized using an experimentally verified model of the PCM devices and achieved performance that is comparable to a floating-point 32-bit model.
Original languageEnglish
Title of host publicationICECS 2020 - 27th IEEE International Conference on Electronics, Circuits and Systems, Proceedings
ISBN (Electronic)9781728160443
DOIs
Publication statusPublished - 23 Nov 2020
Event27th IEEE International Conference on Electronics, Circuits and Systems: ICECS 2020 - Virtual, Glasgow, United Kingdom
Duration: 23 Nov 202025 Nov 2020

Conference

Conference27th IEEE International Conference on Electronics, Circuits and Systems
CountryUnited Kingdom
CityVirtual, Glasgow
Period23/11/2025/11/20

Keywords

  • spiking neural networks
  • spiking neural unit
  • phase-change memory
  • in-memory computing
  • Limited precision
  • Spiking neural unit
  • Phase-change memory
  • Spiking neural networks
  • In-memory computing

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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