BiMDiM: Area efficient Bi-directional MRAM Digital in-Memory Computing

Dongsu Kim, Yunho Jang, Taehwan Kim, Jongsun Park

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

    4 Citations (Scopus)

    Abstract

    Spin transfer torque MRAM (STT-MRAM) based digital in-memory computing (IMC) has been recently proposed for energy efficient processing of convolutional neural network (CNN). The conventional MRAM based IMC architecture suffers from excessive storage area since a large number of intermediate sum and carry bits should be stored for the following successive additions during multiply-accumulate (MAC) operations. In this paper, we propose an area efficient bi-directional MRAM digital IMC (BiMDiM) scheme, where the size of memory cells storing the intermediate sums and carries can be efficiently reduced by repetitively using the same memory cells during MAC operations. In addition, to reduce the number of inefficient half-additions, which can process only two inputs with almost same hardware cost, the addition re-scheduling is also presented to further improve the energy and latency of BiMDiM. The proposed BiMDiM architecture has been simulated using 28nm CMOS process. When compared to the baseline architecture, the proposed BiMDiM improves area efficiency up to 53%.

    Original languageEnglish
    Title of host publicationProceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages74-77
    Number of pages4
    ISBN (Electronic)9781665409964
    DOIs
    Publication statusPublished - 2022
    Event4th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022 - Incheon, Korea, Republic of
    Duration: 2022 Jun 132022 Jun 15

    Publication series

    NameProceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022

    Conference

    Conference4th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022
    Country/TerritoryKorea, Republic of
    CityIncheon
    Period22/6/1322/6/15

    Bibliographical note

    Funding Information:
    This work was supported in part by National R&D Program through the National Research Foundation of Korea funded by Ministry of Science and ICT (NRF-2020M3F3A2A01082591), and in part by the National Research Foundation of Korea grant funded by the Korea government (NRF-2020R1A2C3014820). The EDA tool was supported by the IC Design Education Center(IDEC), Korea.

    Publisher Copyright:
    © 2022 IEEE.

    Keywords

    • Convolutional neural network (CNN)
    • digital in memory computing (digital IMC)
    • Memory area
    • Spin Transfer Torque magnetic random access memory (STT-MRAM)

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Science Applications
    • Computer Vision and Pattern Recognition
    • Hardware and Architecture
    • Human-Computer Interaction
    • Electrical and Electronic Engineering

    Fingerprint

    Dive into the research topics of 'BiMDiM: Area efficient Bi-directional MRAM Digital in-Memory Computing'. Together they form a unique fingerprint.

    Cite this