Low energy domain wall memory based convolution neural network design with optimizing MAC architecture

Jooyoon Kim, Yunho Jang, Taehwan Kim, Jongsun Park

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    Running a convolutional neural network (CNN) algorithm using dedicated integrated circuits (ICs) on real-time portable applications is mainly restricted by slow performance and large power consumption. The power and delay are mainly due to external memory access, which incurs considerable energy consumption and bandwidth issues. In this paper, we propose an efficient convolution layer design using domain wall memory (DWM) for eliminating external memory access in image sensor embedded applications. A low energy access scheme using tag is employed to further reduce power consumption. The experimental results show that the proposed CNN architecture achieves 11.2% memory energy savings and 21.8% of MAC operation reduction compared to conventional architecture.

    Original languageEnglish
    Title of host publication2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781728192017
    DOIs
    Publication statusPublished - 2021
    Event53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Daegu, Korea, Republic of
    Duration: 2021 May 222021 May 28

    Publication series

    NameProceedings - IEEE International Symposium on Circuits and Systems
    Volume2021-May
    ISSN (Print)0271-4310

    Conference

    Conference53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021
    Country/TerritoryKorea, Republic of
    CityDaegu
    Period21/5/2221/5/28

    Bibliographical note

    Publisher Copyright:
    © 2021 IEEE

    Keywords

    • Convolution neural network
    • Domain wall memory

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering

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