ASIC oriented comparative analysis of biologically inspired neuron models

Ahmed J. Abd El-Maksoud, Youssef O. Elmasry, Khaled N. Salama, Hassan Mostafa

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    1 Scopus citations

    Abstract

    This paper introduces the hardware and the ASIC implementations of the four most popular biologically inspired neuron models. The models are quartic, Izhikevich, Hindmarsh Rose and Fitzhugh-Nagumo. Moreover, some approximate computing techniques are applied on these models to reduce the area and power consumption. In addition, ASIC implementations of these models and their approximate versions are carried out. Also, spiking behavior error between these models and the Hodgkin Huxley model, the reference accurate model, is presented. Finally, a fair comparative analysis is discussed to help the Spiking Neural Networks designers to select the best neuron model hardware implementation from the power, area and accuracy perspectives.

    Original languageBritish English
    Title of host publication2018 IEEE 61st International Midwest Symposium on Circuits and Systems, MWSCAS 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages504-507
    Number of pages4
    ISBN (Electronic)9781538673928
    DOIs
    StatePublished - 22 Jan 2019
    Event61st IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2018 - Windsor, Canada
    Duration: 5 Aug 20188 Aug 2018

    Publication series

    NameMidwest Symposium on Circuits and Systems
    Volume2018-August
    ISSN (Print)1548-3746

    Conference

    Conference61st IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2018
    Country/TerritoryCanada
    CityWindsor
    Period5/08/188/08/18

    Keywords

    • Biologically inspired models
    • Neuromorphic computing
    • Spiking neural networks

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