IEEE Journal on Exploratory Solid-State Computational Devices and Circuits (Jan 2019)

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

DOI
https://doi.org/10.1109/JXCDC.2019.2951157
Journal volume & issue
Vol. 5, no. 2
pp. 206 – 214

Abstract

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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.

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