Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends

M. Lakshmi Varshika (Corresponding author), Federico Corradi (Corresponding author), Anup Kumar Das (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)
129 Downloads (Pure)

Abstract

A sustainable computing scenario demands more energy-efficient processors. Neuromorphic systems mimic biological functions by employing spiking neural networks for achieving brain-like efficiency, speed, adaptability, and intelligence. Current trends in neuromorphic technologies address the challenges of investigating novel materials, systems, and architectures for enabling high-integration and extreme low-power brain-inspired computing. This review collects the most recent trends in exploiting the physical properties of nonvolatile memory technologies for implementing efficient in-memory and in-device computing with spike-based neuromorphic architectures.
Original languageEnglish
Article number1610
Number of pages24
JournalElectronics
Volume11
Issue number10
DOIs
Publication statusPublished - 18 May 2022

Keywords

  • nonvolatile memory
  • spiking neural networks
  • neuromorphic computing
  • spiking neural network (SNN)

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