Performance analysis and benchmarking of all-spin spiking neural networks (Special session paper)

Abhronil Sengupta, Aayush Ankit, Kaushik Roy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Spiking Neural Network based brain-inspired computing paradigms are becoming increasingly popular tools for various cognitive tasks. The sparse event-driven processing capability enabled by such networks can be potentially appealing for implementation of low-power neural computing platforms. However, the parallel and memory-intensive computations involved in such algorithms is in complete contrast to the sequential fetch, decode, execute cycles of conventional von-Neumann processors. Recent proposals have investigated the design of spintronic 'in-memory' crossbar based computing architectures driving 'spin neurons' that can potentially alleviate the memory-access bottleneck of CMOS based systems and simultaneously offer the prospect of low-power inner product computations. In this article, we perform a rigorous system-level simulation study of such All-Spin Spiking Neural Networks on a benchmark suite of 6 recognition problems ranging in network complexity from 10k-7.4M synapses and 195-9.2k neurons. System level simulations indicate that the proposed spintronic architecture can potentially achieve ∼1292× energy efficiency and ∼ 235× speedup on average over the benchmark suite in comparison to an optimized CMOS implementation at 45nm technology node.

Original languageEnglish (US)
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4557-4563
Number of pages7
ISBN (Electronic)9781509061815
DOIs
StatePublished - Jun 30 2017
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: May 14 2017May 19 2017

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2017-May

Conference

Conference2017 International Joint Conference on Neural Networks, IJCNN 2017
CountryUnited States
CityAnchorage
Period5/14/175/19/17

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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