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 language||English (US)|
|Number of pages||9|
|Journal||IEEE Journal on Exploratory Solid-State Computational Devices and Circuits|
|State||Published - Dec 2019|
Bibliographical noteFunding 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 (email@example.com) 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.
- Computational random access memory
- pattern matching
- processing in memory
- spin Hall effect (SHE) magnetic tunnel junction (MTJ)