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Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits

Nature communications, 2018-06, Vol.9 (1), p.2331-7, Article 2331 [Peer Reviewed Journal]

2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2018 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-018-04482-4 ;PMID: 29899421

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  • Title:
    Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits
  • Author: Bayat, F Merrikh ; Prezioso, M ; Chakrabarti, B ; Nili, H ; Kataeva, I ; Strukov, D
  • Subjects: Arrays ; Circuits ; Classifiers ; Computer simulation ; Electrodes ; Energy efficiency ; Fabrication ; Functional morphology ; Hardware ; Memristors ; Metal oxides ; Microprocessors ; Multilayer perceptrons ; Neural networks ; Technology
  • Is Part Of: Nature communications, 2018-06, Vol.9 (1), p.2331-7, Article 2331
  • Description: The progress in the field of neural computation hinges on the use of hardware more efficient than the conventional microprocessors. Recent works have shown that mixed-signal integrated memristive circuits, especially their passive (0T1R) variety, may increase the neuromorphic network performance dramatically, leaving far behind their digital counterparts. The major obstacle, however, is immature memristor technology so that only limited functionality has been reported. Here we demonstrate operation of one-hidden layer perceptron classifier entirely in the mixed-signal integrated hardware, comprised of two passive 20 × 20 metal-oxide memristive crossbar arrays, board-integrated with discrete conventional components. The demonstrated network, whose hardware complexity is almost 10× higher as compared to previously reported functional classifier circuits based on passive memristive crossbars, achieves classification fidelity within 3% of that obtained in simulations, when using ex-situ training. The successful demonstration was facilitated by improvements in fabrication technology of memristors, specifically by lowering variations in their I-V characteristics.
  • Publisher: England: Nature Publishing Group
  • Language: English
  • Identifier: ISSN: 2041-1723
    EISSN: 2041-1723
    DOI: 10.1038/s41467-018-04482-4
    PMID: 29899421
  • Source: PubMed Central
    ProQuest Central
    DOAJ Directory of Open Access Journals
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