Using Spin-Hall MTJs to Build an Energy-Efficient In-memory Computation Platform

Masoud Zabihi, Zhengyang Zhao, D. C. Mahendra, Zamshed I. Chowdhury, Salonik Resch, Thomas Peterson, Ulya R. Karpuzcu, Jian Ping Wang, Sachin S. Sapatnekar

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

10 Scopus citations

Abstract

We present the Spin Hall Effect (SHE) Computational Random Access Memory (CRAM) for in-memory computation, incorporating considerations at the device, gate, and functional levels. For two specific applications (2-D convolution and neuromorphic digit recognition), we show that SHE-CRAM is 3x faster and has over 4x lower energy than a prior STT-based CRAM implementation, and is over 2000x faster and at least 130x more energy-efficient than state-of-the-art near-memory processing.

Original languageEnglish (US)
Title of host publicationProceedings of the 20th International Symposium on Quality Electronic Design, ISQED 2019
PublisherIEEE Computer Society
Pages52-57
Number of pages6
ISBN (Electronic)9781728103921
DOIs
StatePublished - Apr 23 2019
Event20th International Symposium on Quality Electronic Design, ISQED 2019 - Santa Clara, United States
Duration: Mar 6 2019Mar 7 2019

Publication series

NameProceedings - International Symposium on Quality Electronic Design, ISQED
Volume2019-March
ISSN (Print)1948-3287
ISSN (Electronic)1948-3295

Conference

Conference20th International Symposium on Quality Electronic Design, ISQED 2019
CountryUnited States
CitySanta Clara
Period3/6/193/7/19

Keywords

  • In-memory computing
  • Neuromorphic computing
  • Nonvolatile memory
  • SHE-CRAM
  • Spintronics

Fingerprint Dive into the research topics of 'Using Spin-Hall MTJs to Build an Energy-Efficient In-memory Computation Platform'. Together they form a unique fingerprint.

Cite this