Brain-inspired computing with memristors: Challenges in devices, circuits, and systems

Yang Zhang, Zhongrui Wang, Jiadi Zhu, Yuchao Yang, Mingyi Rao, Wenhao Song, Ye Zhuo, Xumeng Zhang, Menglin Cui, Linlin Shen, Ru Huang, J. Joshua Yang

Research output: Journal PublicationReview articlepeer-review

108 Citations (Scopus)

Abstract

This article provides a review of current development and challenges in brain-inspired computing with memristors. We review the mechanisms of various memristive devices that can mimic synaptic and neuronal functionalities and survey the progress of memristive spiking and artificial neural networks. Different architectures are compared, including spiking neural networks, fully connected artificial neural networks, convolutional neural networks, and Hopfield recurrent neural networks. Challenges and strategies for nanoelectronic brain-inspired computing systems, including device variations, training, and testing algorithms, are also discussed.

Original languageEnglish
Article number011308
JournalApplied Physics Reviews
Volume7
Issue number1
DOIs
Publication statusPublished - 1 Mar 2020
Externally publishedYes

ASJC Scopus subject areas

  • Physics and Astronomy (all)

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