Low cost hybrid spin-CMOS compressor for stochastic neural networks

Bingzhe Li, Jiaxi Hu, M. Hassan Najafi, Steven Koester, David J. Lilja

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

2 Scopus citations


With expansion of neural network (NN) applications lowering their hardware implementation cost becomes an urgent task especially in back-end applications where the power-supply is limited. Stochastic computing (SC) is a promising solution to realize low-cost hardware designs. Implementation of matrix multiplication has been a bottleneck in previous stochastic neural networks (SC-NNs). In this paper, we introduce spintronic components into the design of SC-NNs. A novel spin-CMOS matrix multiplier is proposed in which the stochastic multiplications are performed by CMOS AND gates while the sum of products is implemented by spintronic compressor gates. The experimental results indicate that compared to the conventional binary implementations the proposed hybrid spin-CMOS architecture can achieve over 125x, 4.5x and 43x; reduction in terms of power, energy and area consumptions, respectively. Moreover, compared to previous CMOS-based SC-NNs, our design saves the power by 3.1x-7.3x, reduces energy consumption by 3.1x-7.3x and decreases area by 1.4x-7.6x while maintaining similar recognition rates.

Original languageEnglish (US)
Title of host publicationGLSVLSI 2019 - Proceedings of the 2019 Great Lakes Symposium on VLSI
PublisherAssociation for Computing Machinery
Number of pages6
ISBN (Electronic)9781450362528
StatePublished - May 13 2019
Event29th Great Lakes Symposium on VLSI, GLSVLSI 2019 - Tysons Corner, United States
Duration: May 9 2019May 11 2019

Publication series

NameProceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI


Conference29th Great Lakes Symposium on VLSI, GLSVLSI 2019
Country/TerritoryUnited States
CityTysons Corner

Bibliographical note

Funding Information:
This work was supported in part by National Science Foundation grant no. CCF-1408123 and Seagate Technology.

Publisher Copyright:
© 2019 ACM.


  • Compressor
  • Neural network
  • Spintronic
  • Stochastic computing


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