Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays

Yuhan Shi; Leon Nguyen; Sangheon Oh; Xin Liu; Foroozan Koushan; John R. Jameson; Duygu Kuzum

doi:10.1038/s41467-018-07682-0

Nature Communications (Dec 2018)

Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays

Yuhan Shi,
Leon Nguyen,
Sangheon Oh,
Xin Liu,
Foroozan Koushan,
John R. Jameson,
Duygu Kuzum

Affiliations

Yuhan Shi: Department of Electrical and Computer Engineering, University of California, San Diego
Leon Nguyen: Department of Electrical and Computer Engineering, University of California, San Diego
Sangheon Oh: Department of Electrical and Computer Engineering, University of California, San Diego
Xin Liu: Department of Electrical and Computer Engineering, University of California, San Diego
Foroozan Koushan: Adesto Technologies Corporation
John R. Jameson: Adesto Technologies Corporation
Duygu Kuzum: Department of Electrical and Computer Engineering, University of California, San Diego

DOI: https://doi.org/10.1038/s41467-018-07682-0
Journal volume & issue: Vol. 9, no. 1
pp. 1 – 11

Abstract

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To realize the potential of resistive RAM crossbar arrays as platforms for neuromorphic computing, reduced network-level energy consumption must be achieved. Here, the authors use a hardware/software co-design approach to realize reduced energy consumption during network training for the network.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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