Energy-Efficient SNN Implementation Using RRAM-Based Computation In-Memory (CIM)

Asmae El Arrassi*, Anteneh Gebregiorgis, Anass El Haddadi*, Said Hamdioui

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Spiking Neural Networks (SNNs) can drastically improve the energy efficiency of neuromorphic computing through network sparsity and event-driven execution. Thus, SNNs have the potential to support practical cognitive tasks on resource constrained platforms, such as edge devices. To realize this, SNN requires energy-efficient hardware which can run applications with a limited energy budget. However, the conventional CMOS implementations cannot achieve this goal due to the various architectural and technological challenges. In this work, we address these issues by developing an energy-efficient and accurate SNN hardware based on Computation In-Memory (CIM) architecture using Resistive Random Access Memory (RRAM) devices. The developed SNN architecture is based on unsupervised Spike Time Dependent Plasticity (STDP) learning algorithm with online learning capability. Simulation results show that the proposed architecture is energy-efficient with a consumption of ≈20 fJ per spike, while maintaining state-of-the-art inference accuracy of 95% when evaluated using the MNIST dataset.
Original languageEnglish
Title of host publicationProceedings of the 2022 IFIP/IEEE 30th International Conference on Very Large Scale Integration (VLSI-SoC)
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)978-1-6654-9005-4
ISBN (Print)978-1-6654-9006-1
DOIs
Publication statusPublished - 2022
Event2022 IFIP/IEEE 30th International Conference on Very Large Scale Integration (VLSI-SoC) - Patras, Greece
Duration: 3 Oct 20225 Oct 2022

Conference

Conference2022 IFIP/IEEE 30th International Conference on Very Large Scale Integration (VLSI-SoC)
Country/TerritoryGreece
CityPatras
Period3/10/225/10/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • SNN
  • RRAM
  • In-Memory Computing
  • STDP

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