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    ALL-OPTICAL NONLINEAR ACTIVATION FUNCTION FOR PHOTONIC NEURAL NETWORKS

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    1810.01216.pdf (5.255Mb)
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    https://arxiv.org/ftp/arxiv/papers/1810/1810.01216.pdf
    Permanent Link
    http://hdl.handle.net/11603/11768
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    • UMBC Physics Department
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    Author/Creator
    Miscuglio, Mario
    Mehrabian, Armin
    Hu, Zibo
    Azzam, Shaimaa I.
    George, Jonathan
    Kildishev, Alexander V.
    Pelton, Matthew
    Sorger, Volker J.
    Date
    2018
    Type of Work
    13 pages
    Text
    Journal Article
    Citation of Original Publication
    Mario Miscuglio , Armin Mehrabian , Zibo Hu , Shaimaa i. Azzam, Jonathan George, Alexander v. kildishev, Matthew pelton, Volker J. Sorger, ALL-OPTICAL NONLINEAR ACTIVATION FUNCTION FOR PHOTONIC NEURAL NETWORKS, 2018, https://arxiv.org/ftp/arxiv/papers/1810/1810.01216.pdf
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    This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
    © 2018 Optical Society of America. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved
    Subjects
    OPTICAL
    NONLINEAR
    PHOTONIC NEURAL NETWORKS
    Nano-particle
    Abstract
    With the recent successes of neural networks (NN) to perform machine-learning tasks, photonic-based NN designs may enable high throughput and low power neuromorphic compute paradigms since they bypass the parasitic charging of capacitive wires. Thus, engineering data-information processors capable of executing NN algorithms with high efficiency is of major importance for applications ranging from pattern recognition to classification. Our hypothesis is therefore, that if the time-limiting electro-optic conversion of current photonic NN designs could be postponed until the very end of the network, then the execution time of the photonic algorithm is simple the delay of the time-of-flight of photons through the NN, which is on the order of picoseconds for integrated photonics. Exploring such all-optical NN, in this work we discuss two independent approaches of implementing the optical perceptron’s nonlinear activation function based on nanophotonic structures exhibiting i) induced transparency and ii) reverse saturated absorption. Our results show that the all-optical nonlinearity provides about 3 and 7 dB extinction ratio for the two systems considered, respectively, and classification accuracies of an exemplary MNIST task of 97% and near 100% are found, which rivals that of software based trained NNs, yet with ignored noise in the network. Together with a developed concept for an all-optical perceptron, these findings point to the possibility of realizing pure photonic NNs with potentially unmatched throughput and even energy consumption for next generation information processing hardware.


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.