Analog CMOS-based resistive processing unit for deep neural network training

Seyoung Kim, Tayfun Gokmen, Hyung Min Lee, Wilfried E. Haensch

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

    48 Citations (Scopus)

    Abstract

    Recently we have shown that an architecture based on resistive processing unit (RPU) devices has potential to achieve significant acceleration in deep neural network (DNN) training compared to today's software-based DNN implementations running on CPU/GPU. However, currently available device candidates based on non-volatile memory technologies do not satisfy all the requirements to realize the RPU concept. Here, we propose an analog CMOS-based RPU design (CMOS RPU) which can store and process data locally and can be operated in a massively parallel manner. We analyze various properties of the CMOS RPU to evaluate the functionality and feasibility for acceleration of DNN training.

    Original languageEnglish
    Title of host publication2017 IEEE 60th International Midwest Symposium on Circuits and Systems, MWSCAS 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages422-425
    Number of pages4
    ISBN (Electronic)9781509063895
    DOIs
    Publication statusPublished - 2017 Sept 27
    Event60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017 - Boston, United States
    Duration: 2017 Aug 62017 Aug 9

    Publication series

    NameMidwest Symposium on Circuits and Systems
    Volume2017-August
    ISSN (Print)1548-3746

    Conference

    Conference60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017
    Country/TerritoryUnited States
    CityBoston
    Period17/8/617/8/9

    Bibliographical note

    Publisher Copyright:
    © 2017 IEEE.

    Keywords

    • Deep neural network
    • Machine learning accelerator
    • RPU
    • Resistive memory
    • Resistive processing unit

    ASJC Scopus subject areas

    • Electronic, Optical and Magnetic Materials
    • Electrical and Electronic Engineering

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