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

Author

Listed:
  • 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)

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.

Suggested Citation

  • M. Prezioso & F. Merrikh-Bayat & B. D. Hoskins & G. C. Adam & K. K. Likharev & D. B. Strukov, 2015. "Training and operation of an integrated neuromorphic network based on metal-oxide memristors," Nature, Nature, vol. 521(7550), pages 61-64, May.
    • Handle: RePEc:nat:nature:v:521:y:2015:i:7550:d:10.1038_nature14441
    DOI: 10.1038/nature14441
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