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    Memristor-Based Synaptic Sampling Machines

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    Type
    Conference Paper
    Authors
    Dolzhikova, I.
    Salama, Khaled N. cc
    Kizheppatt, V.
    James, A.
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Date
    2019-02-28
    Online Publication Date
    2019-02-28
    Print Publication Date
    2018-07
    Permanent link to this record
    http://hdl.handle.net/10754/652973
    
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    Abstract
    Synaptic Sampling Machine (SSM) is a type of neural network model that considers biological unreliability of the synapses. We propose the circuit design of the SSM neural network which is realized through the memristive-CMOS crossbar structure with the synaptic sampling cell (SSC) being used as a basic stochastic unit. The increase in the edge computing devices in the Internet of things era, drives the need for hardware acceleration for data processing and computing. The computational considerations of the processing speed and possibility for the real-time realization pushes the synaptic sampling algorithm that demonstrated promising results on software for hardware implementation.
    Citation
    Dolzhikova I, Salama K, Kizheppatt V, James A (2018) Memristor-Based Synaptic Sampling Machines. 2018 IEEE 18th International Conference on Nanotechnology (IEEE-NANO). Available: http://dx.doi.org/10.1109/NANO.2018.8626382.
    Sponsors
    We would like to express special thanks to Rawan Nouz for the initial ideas, technical support and useful discussions on the topic of synaptic sampling. In addition to that, we thank Umesh Chand and ulrich Buttner for guidance and support.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2018 IEEE 18th International Conference on Nanotechnology (IEEE-NANO)
    Conference/Event name
    18th International Conference on Nanotechnology, NANO 2018
    DOI
    10.1109/NANO.2018.8626382
    arXiv
    1808.00679
    Additional Links
    https://ieeexplore.ieee.org/document/8626382
    http://arxiv.org/pdf/1808.00679
    ae974a485f413a2113503eed53cd6c53
    10.1109/NANO.2018.8626382
    Scopus Count
    Collections
    Conference Papers; Electrical and Computer Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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