Efficient and self-adaptive in-situ learning in multilayer memristor neural networks

Memristor-based neural networks hold promise for neuromorphic computing, yet large-scale experimental execution remains difficult. Here, Xia et al. create a multi-layer memristor neural network with in-situ machine learning and achieve competitive image classification accuracy on a standard dataset.

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Detalles Bibliográficos
Autores principales:	Can Li, Daniel Belkin, Yunning Li, Peng Yan, Miao Hu, Ning Ge, Hao Jiang, Eric Montgomery, Peng Lin, Zhongrui Wang, Wenhao Song, John Paul Strachan, Mark Barnell, Qing Wu, R. Stanley Williams, J. Joshua Yang, Qiangfei Xia
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/b4e8c66198ed41eea8aa429291ec599c
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spelling	oai:doaj.org-article:b4e8c66198ed41eea8aa429291ec599c2021-12-02T13:23:48ZEfficient and self-adaptive in-situ learning in multilayer memristor neural networks10.1038/s41467-018-04484-22041-1723https://doaj.org/article/b4e8c66198ed41eea8aa429291ec599c2018-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-04484-2https://doaj.org/toc/2041-1723Memristor-based neural networks hold promise for neuromorphic computing, yet large-scale experimental execution remains difficult. Here, Xia et al. create a multi-layer memristor neural network with in-situ machine learning and achieve competitive image classification accuracy on a standard dataset.Can LiDaniel BelkinYunning LiPeng YanMiao HuNing GeHao JiangEric MontgomeryPeng LinZhongrui WangWenhao SongJohn Paul StrachanMark BarnellQing WuR. Stanley WilliamsJ. Joshua YangQiangfei XiaNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-8 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Can Li Daniel Belkin Yunning Li Peng Yan Miao Hu Ning Ge Hao Jiang Eric Montgomery Peng Lin Zhongrui Wang Wenhao Song John Paul Strachan Mark Barnell Qing Wu R. Stanley Williams J. Joshua Yang Qiangfei Xia Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
description	Memristor-based neural networks hold promise for neuromorphic computing, yet large-scale experimental execution remains difficult. Here, Xia et al. create a multi-layer memristor neural network with in-situ machine learning and achieve competitive image classification accuracy on a standard dataset.
format	article
author	Can Li Daniel Belkin Yunning Li Peng Yan Miao Hu Ning Ge Hao Jiang Eric Montgomery Peng Lin Zhongrui Wang Wenhao Song John Paul Strachan Mark Barnell Qing Wu R. Stanley Williams J. Joshua Yang Qiangfei Xia
author_facet	Can Li Daniel Belkin Yunning Li Peng Yan Miao Hu Ning Ge Hao Jiang Eric Montgomery Peng Lin Zhongrui Wang Wenhao Song John Paul Strachan Mark Barnell Qing Wu R. Stanley Williams J. Joshua Yang Qiangfei Xia
author_sort	Can Li
title	Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
title_short	Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
title_full	Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
title_fullStr	Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
title_full_unstemmed	Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
title_sort	efficient and self-adaptive in-situ learning in multilayer memristor neural networks
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/b4e8c66198ed41eea8aa429291ec599c
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Efficient and self-adaptive in-situ learning in multilayer memristor neural networks

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