Nature Communications (Jun 2018)
Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
- 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
Affiliations
- Can Li
- Department of Electrical and Computer Engineering, University of Massachusetts
- Daniel Belkin
- Department of Electrical and Computer Engineering, University of Massachusetts
- Yunning Li
- Department of Electrical and Computer Engineering, University of Massachusetts
- Peng Yan
- Department of Electrical and Computer Engineering, University of Massachusetts
- Miao Hu
- Hewlett Packard Labs, Hewlett Packard Enterprise
- Ning Ge
- HP Labs, HP Inc.
- Hao Jiang
- Department of Electrical and Computer Engineering, University of Massachusetts
- Eric Montgomery
- Hewlett Packard Labs, Hewlett Packard Enterprise
- Peng Lin
- Department of Electrical and Computer Engineering, University of Massachusetts
- Zhongrui Wang
- Department of Electrical and Computer Engineering, University of Massachusetts
- Wenhao Song
- Department of Electrical and Computer Engineering, University of Massachusetts
- John Paul Strachan
- Hewlett Packard Labs, Hewlett Packard Enterprise
- Mark Barnell
- Air Force Research Laboratory, Information Directorate
- Qing Wu
- Air Force Research Laboratory, Information Directorate
- R. Stanley Williams
- Hewlett Packard Labs, Hewlett Packard Enterprise
- J. Joshua Yang
- Department of Electrical and Computer Engineering, University of Massachusetts
- Qiangfei Xia
- Department of Electrical and Computer Engineering, University of Massachusetts
- DOI
- https://doi.org/10.1038/s41467-018-04484-2
- Journal volume & issue
-
Vol. 9,
no. 1
pp. 1 – 8
Abstract
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.
