Content addressable memory based binarized neural network accelerator using time-domain signal processing

Woong Choi, Kwanghyo Jeong, Kyungrak Choi, Kyeongho Lee, Jongsun Park

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

    16 Citations (Scopus)

    Abstract

    Binarized neural network (BNN) is one of the most promising solution for low-cost convolutional neural network acceleration. Since BNN is based on binarized bit-level operations, there exist great opportunities to reduce power-hungry data transfers and complex arithmetic operations. In this paper, we propose a content addressable memory (CAM) based BNN accelerator. By using time-domain signal processing, the huge convolution operations of BNN can be effectively replaced to the CAM search operation. In addition, thanks to fully parallel search of CAM, the parallel convolution operations for non-overlapped filtering window is enabled for high throughput data processing. To verify the effectiveness of the proposed CAM based BNN accelerator, the convolutional layer of LeNet-5 model has been implemented using 65nm CMOS technology. The implementation results show that the proposed BNN accelerator achieves 9.4% and 38.5% of area and energy savings, respectively. The parallel convolution operation of the proposed approach also shows 2.4x improved processing time.

    Original languageEnglish
    Title of host publicationProceedings of the 55th Annual Design Automation Conference, DAC 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Print)9781450357005
    DOIs
    Publication statusPublished - 2018 Jun 24
    Event55th Annual Design Automation Conference, DAC 2018 - San Francisco, United States
    Duration: 2018 Jun 242018 Jun 29

    Publication series

    NameProceedings - Design Automation Conference
    VolumePart F137710
    ISSN (Print)0738-100X

    Other

    Other55th Annual Design Automation Conference, DAC 2018
    Country/TerritoryUnited States
    CitySan Francisco
    Period18/6/2418/6/29

    Bibliographical note

    Publisher Copyright:
    © 2018 Association for Computing Machinery.

    Keywords

    • Binarized neural network
    • Content addressable memory
    • Timedomain signal processing

    ASJC Scopus subject areas

    • Computer Science Applications
    • Control and Systems Engineering
    • Electrical and Electronic Engineering
    • Modelling and Simulation

    Fingerprint

    Dive into the research topics of 'Content addressable memory based binarized neural network accelerator using time-domain signal processing'. Together they form a unique fingerprint.

    Cite this