Spin-Hall-Effect-Based Stochastic Number Generator for Parallel Stochastic Computing

Jiaxi Hu, Bingzhe Li, Cong Ma, David Lilja, Steven J. Koester

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Stochastic computing (SC) is a promising technology that can be used for low-cost hardware designs. However, SC suffers from its long latency. Although parallel processing can efficiently shorten the latency, duplicated stochastic number generators (SNGs) are necessary, which cause substantial hardware overhead. This paper proposes a scalable SNG based on the spin-Hall-effect (SHE), which is capable of generating multiple independent stochastic streams simultaneously. The design takes advantages of the efficient charge-to-spin conversion from the Spin-Hall material and the intrinsic stochasticity of nanomagnets. Compared to previous spintronic SNGs, the SHE-SNG can reduce the area by 1.6×-7.8× and the power by 4.9×-13× while increasing the degree of parallelism from 1 to 16. Compared to CMOS-based SNGs, the proposed SNG obtained 24×-120× and 53× reduction in terms of area and power, respectively. Finally, three benchmarks were implemented, and the results indicate that SC implementations with the proposed SHE-SNG can achieve 1.2×-29× reduction of hardware resources compared to implementations with previous CMOS-and spintronic-based designs while scaling the degree of parallelism from 1 to 64.

Original languageEnglish (US)
Article number8741170
Pages (from-to)3620-3627
Number of pages8
JournalIEEE Transactions on Electron Devices
Issue number8
StatePublished - Aug 2019

Bibliographical note

Funding Information:
Manuscript received April 26, 2019; revised May 26, 2019; accepted May 28, 2019. Date of publication June 19, 2019; date of current version July 23, 2019. This work was supported in part by the National Science Foundation under Grant CCF-1408123. The review of this paper was arranged by Editor A. M. P. Anantram. (Jiaxi Hu and Bingzhe Li contributed equally to this work.) (Corresponding author: Bingzhe Li.) The authors are with the Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55445 USA (e-mail: huxxx499@umn.edu; lixx1743@umn.edu; maxxx376@umn.edu; lilja@umn.edu; skoester@umn.edu).

Publisher Copyright:
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  • Parallel processing
  • spintronic
  • stochastic computing (SC)
  • stochastic number generator (SNG)


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