Training and operation of an integrated neuromorphic network based on metal-oxide memristors

M. Prezioso (), F. Merrikh-Bayat, B. D. Hoskins, G. C. Adam, K. K. Likharev and D. B. Strukov ()
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M. Prezioso: University of California at Santa Barbara
F. Merrikh-Bayat: University of California at Santa Barbara
B. D. Hoskins: University of California at Santa Barbara
G. C. Adam: University of California at Santa Barbara
K. K. Likharev: Stony Brook University
D. B. Strukov: University of California at Santa Barbara

Nature, 2015, vol. 521, issue 7550, 61-64

Abstract: A transistor-free metal-oxide memristor crossbar with low device variability is realised and trained to perform a simple classification task, opening the way to integrated neuromorphic networks of a complexity comparable to that of the human brain, with high operational speed and manageable power dissipation.

Date: 2015
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DOI: 10.1038/nature14441

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