Domain Wall Memory based Convolutional Neural Networks for Bit-width Extendability and Energy-Efficiency

Jinil Chung, Jongsun Park, Swaroop Ghosh

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

11 Scopus citations

Abstract

In the hardware implementation of deep learning algorithms such as Convolutional Neural Networks (CNNs), vector-vector multiplications and memories for storing parameters take a significant portion of area and power consumption. In this paper, we propose a Domain Wall Memory (DWM) based design of CNN convolutional layer. In the proposed design, the resistive cell sensing mechanism is efficiently exploited to design a low-cost DWM-based cell arrays for storing parameters. The unique serial access mechanism and small footprint of DWM are also used to reduce the area and power cost of the input registers for aligning inputs. Contrary to the conventional implementation using Memristor-Based Crossbar (MBC), the bit-width of the proposed CNN convolutional layer is extendable for high resolution classifications and training. Simulation results using 65 nm CMOS process show that the proposed design archives 34% of energy savings compared to the conventional MBC based design approach.

Original languageEnglish (US)
Title of host publicationISLPED 2016 - Proceedings of the 2016 International Symposium on Low Power Electronics and Design
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages332-337
Number of pages6
ISBN (Electronic)9781450341851
DOIs
StatePublished - Aug 8 2016
Event21st IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2016 - San Francisco, United States
Duration: Aug 8 2016Aug 10 2016

Publication series

NameProceedings of the International Symposium on Low Power Electronics and Design
ISSN (Print)1533-4678

Other

Other21st IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2016
CountryUnited States
CitySan Francisco
Period8/8/168/10/16

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Fingerprint Dive into the research topics of 'Domain Wall Memory based Convolutional Neural Networks for Bit-width Extendability and Energy-Efficiency'. Together they form a unique fingerprint.

  • Cite this

    Chung, J., Park, J., & Ghosh, S. (2016). Domain Wall Memory based Convolutional Neural Networks for Bit-width Extendability and Energy-Efficiency. In ISLPED 2016 - Proceedings of the 2016 International Symposium on Low Power Electronics and Design (pp. 332-337). (Proceedings of the International Symposium on Low Power Electronics and Design). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1145/2934583.2934602