Neuromorphic computing with antiferromagnetic spintronics

Aleksandr Kurenkov, Shunsuke Fukami, Hideo Ohno

Research output: Contribution to journalReview article

Abstract

While artificial intelligence, capable of readily addressing cognitive tasks, has transformed technologies and daily lives, there remains a huge gap with biological systems in terms of performance per energy unit. Neuromorphic computing, in which hardware with alternative architectures, circuits, devices, and/or materials is explored, is expected to reduce the gap. Antiferromagnetic spintronics could offer a promising platform for this scheme. Active functionalities of antiferromagnetic systems have been demonstrated recently and several works indicated their potential for biologically inspired computing. In this perspective, we look through the prism of these works and discuss prospects and challenges of antiferromagnetic spintronics for neuromorphic computing. Overview and discussion are given on non-spiking artificial neural networks, spiking neural networks, and reservoir computing.

Original languageEnglish
Article number010902
JournalJournal of Applied Physics
Volume128
Issue number1
DOIs
Publication statusPublished - 2020 Jul 7

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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