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) |
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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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