Role of resistive memory devices in brain-inspired computing

Sabina Spiga, Abu Sebastian, Damien Querlioz, Bipin Rajendran

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations

Abstract

This chapter introduces the current state of the art of memristive devices and their roles in emerging computational paradigms. The materials systems and device characteristics of various resistive switching memories, also named memristive device technologies, including PCM, RRAM, MRAM, and FeRAM, are presented and discussed in terms of functionality, switching mechanism, and perspectives for applications beyond storage, toward brain-inspired computing. Conventional digital computers face increasing difficulties in performance and power efficiency due to their von Neumann architecture. Memristive devices are therefore considered as promising hardware building blocks for data and memory-centric computing schemes. This chapter provides an overview of currently enabled novel applications exploiting device properties.

Original languageEnglish (US)
Title of host publicationMemristive Devices for Brain-Inspired Computing
Subtitle of host publicationFrom Materials, Devices, and Circuits to Applications - Computational Memory, Deep Learning, and Spiking Neural Networks
PublisherElsevier
Pages3-16
Number of pages14
ISBN (Electronic)9780081027820
DOIs
StatePublished - Jan 1 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Keywords

  • Analog computing
  • Deep neural networks
  • FeFET
  • FeRAM
  • In-memory computing
  • In-memory logic
  • Memristive devices
  • MRAM
  • MTJ
  • Neural networks
  • Neuromorphic computing
  • PCM
  • Resistive switching memory
  • RRAM
  • Spiking neural networks

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