Towards Efficient RRAM-based Quantized Neural Networks Hardware: State-of-the-art and Open Issues

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

1 Scopus citations

Abstract

The increasing amount of data processed on edge and demand for reducing the energy consumption for large neural network architectures have initiated the transition from traditional von Neumann architectures towards in-memory computing paradigms. Quantization is one of the methods to reduce power and computation requirements for neural networks by limiting bit precision. Resistive Random Access Memory (RRAM) devices are great candidates for Quantized Neural Networks (QNN) implementations. As the number of possible conductive states in RRAMs is limited, a certain level of quantization is always considered when designing RRAM-based neural networks. In this work, we provide a comprehensive analysis of state-of-the-art RRAM-based QNN implementations, showing where RRAMs stand in terms of satisfying the criteria of efficient QNN hardware. We cover hardware and device challenges related to QNNs and show the main unsolved issues and possible future research directions.

Original languageEnglish (US)
Title of host publication2022 IEEE 22nd International Conference on Nanotechnology, NANO 2022
PublisherIEEE Computer Society
Pages465-468
Number of pages4
ISBN (Electronic)9781665452250
DOIs
StatePublished - 2022
Event22nd IEEE International Conference on Nanotechnology, NANO 2022 - Palma de Mallorca, Spain
Duration: Jul 4 2022Jul 8 2022

Publication series

NameProceedings of the IEEE Conference on Nanotechnology
Volume2022-July
ISSN (Print)1944-9399
ISSN (Electronic)1944-9380

Conference

Conference22nd IEEE International Conference on Nanotechnology, NANO 2022
Country/TerritorySpain
CityPalma de Mallorca
Period07/4/2207/8/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Hardware
  • QNN
  • Quantization
  • RRAM

ASJC Scopus subject areas

  • Bioengineering
  • Electrical and Electronic Engineering
  • Materials Chemistry
  • Condensed Matter Physics

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