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

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....

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Veröffentlicht in:Applied Physics Reviews 2020-03, Vol.7 (1)
Hauptverfasser: Zhang, Yang, Wang, Zhongrui, Zhu, Jiadi, Yang, Yuchao, Rao, Mingyi, Song, Wenhao, Zhuo, Ye, Zhang, Xumeng, Cui, Menglin, Shen, Linlin, Huang, Ru, Joshua Yang, J.
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container_title Applied Physics Reviews
container_volume 7
creator Zhang, Yang
Wang, Zhongrui
Zhu, Jiadi
Yang, Yuchao
Rao, Mingyi
Song, Wenhao
Zhuo, Ye
Zhang, Xumeng
Cui, Menglin
Shen, Linlin
Huang, Ru
Joshua Yang, J.
description 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.
doi_str_mv 10.1063/1.5124027
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title Brain-inspired computing with memristors: Challenges in devices, circuits, and systems
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