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    Analog Backpropagation Learning Circuits for Memristive Crossbar Neural Networks

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    Type
    Conference Paper
    Authors
    Krestinskaya, Olga
    Salama, Khaled N. cc
    James, Alex Pappachen
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Date
    2018-05-04
    Online Publication Date
    2018-05-04
    Print Publication Date
    2018-05
    Permanent link to this record
    http://hdl.handle.net/10754/630386
    
    Metadata
    Show full item record
    Abstract
    The implementation of backpropagation algorithm using gradient descent operation with analog circuits is an open problem. In this paper, we present the analog learning circuits for realizing backpropagation algorithm for use with neural networks in memristive crossbar arrays. The circuits are simulated in SPICE using TSMC 180nm CMOS process models, and HP memristor models. The gradient descent operations are validated comprehensively using the relevant transfer characteristics and transient response of individual circuit modules.
    Citation
    Krestinskaya O, Salama KN, James AP (2018) Analog Backpropagation Learning Circuits for Memristive Crossbar Neural Networks. 2018 IEEE International Symposium on Circuits and Systems (ISCAS). Available: http://dx.doi.org/10.1109/iscas.2018.8351344.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2018 IEEE International Symposium on Circuits and Systems (ISCAS)
    DOI
    10.1109/iscas.2018.8351344
    Additional Links
    https://ieeexplore.ieee.org/document/8351344/
    ae974a485f413a2113503eed53cd6c53
    10.1109/iscas.2018.8351344
    Scopus Count
    Collections
    Conference Papers; Electrical and Computer Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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