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Nat Commun. 2018 Jun 19;9(1):2385. doi: 10.1038/s41467-018-04484-2.

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

Author information

1
Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA.
2
Swarthmore College, Swarthmore, PA, 19081, USA.
3
Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
4
Hewlett Packard Labs, Hewlett Packard Enterprise, Palo Alto, CA, 94304, USA.
5
Department of Electrical and Computer Engineering, Binghamton University, Binghamton, NY, 13902, USA.
6
HP Labs, HP Inc., Palo Alto, CA, 94304, USA.
7
Air Force Research Laboratory, Information Directorate, Rome, NY, 13441, USA.
8
Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA. jjyang@umass.edu.
9
Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA. qxia@umass.edu.

Abstract

Memristors with tunable resistance states are emerging building blocks of artificial neural networks. However, in situ learning on a large-scale multiple-layer memristor network has yet to be demonstrated because of challenges in device property engineering and circuit integration. Here we monolithically integrate hafnium oxide-based memristors with a foundry-made transistor array into a multiple-layer neural network. We experimentally demonstrate in situ learning capability and achieve competitive classification accuracy on a standard machine learning dataset, which further confirms that the training algorithm allows the network to adapt to hardware imperfections. Our simulation using the experimental parameters suggests that a larger network would further increase the classification accuracy. The memristor neural network is a promising hardware platform for artificial intelligence with high speed-energy efficiency.

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