Spintronic In-Memory Pattern Matching

Zamshed I. Chowdhury, S. Karen Khatamifard, Zhengyang Zhao, Masoud Zabihi, Salonik Resch, Meisam Razaviyayn, Jian Ping Wang, Sachin Sapatnekar, Ulya R. Karpuzcu

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Traditional Von Neumann computing is falling apart in the era of exploding data volumes as the overhead of data transfer becomes forbidding. Instead, it is more energy-efficient to fuse compute capability with memory where the data reside. This is particularly critical to pattern matching, a key computational step in large-scale data analytics, which involves repetitive search over very large databases residing in memory. Emerging spintronic technologies show remarkable versatility for the tight integration of logic and memory. In this article, we introduce SpinPM, a novel high-density, reconfigurable spintronic in-memory pattern matching spin-orbit torque (SOT)-specifically spin Hall effect (SHE)-substrate, and demonstrate the performance benefit SpinPM can achieve over conventional and near-memory processing systems.

Original languageEnglish (US)
Article number8890687
Pages (from-to)206-214
Number of pages9
JournalIEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Issue number2
StatePublished - Dec 2019

Bibliographical note

Funding Information:
1Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455 USA 2Department of Industrial & Systems Engineering, University of Southern California, Los Angeles, CA 90089 USA CORRESPONDING AUTHOR: Z. I. CHOWDHURY (chowh005@umn.edu) This work was supported in part by NSF under Grant SPX-1725420. This article has supplementary downloadable material available at http://ieeexplore.ieee.org, provided by the authors.

Publisher Copyright:
© 2014 IEEE.


  • Computational random access memory
  • pattern matching
  • processing in memory
  • spin Hall effect (SHE) magnetic tunnel junction (MTJ)


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