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Spintronic Solutions for Stochastic Computing

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Stochastic Computing: Techniques and Applications

Abstract

With the rising requirements of computation efficiencies for artificial intelligence applications, the conventional deterministic computation approach has shown many bottlenecks in developing large scale deep learning algorithms. Especially for Bayesian inference problems with uncertainty and incompleteness, it usually requires many sampling operations which largely degrade the inference efficiencies. In this chapter, a spintronic devices based stochastic computing method is presented for efficient Bayesian inference. Stochastic computing is regarded as a promising approach to improve the area and energy efficiencies with simplified arithmetic operations. Spintronic devices are utilized to realize efficient sampling operations to overcome the inference efficiencies in terms of power, area and speed. The intrinsic randomness existing in switching process of spintronic device is exploited to realize stochastic number generator, which is the critical block for efficient circuit design of Bayesian inference. A device-to-architecture level framework is proposed to evaluate the promised performance of spintronic device based Bayesian inference system.

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Author information

Authors and Affiliations

  1. Fert Beijing Institute, BDBC and School of Microelectronics, Beihang University, Beijing, China

    Xiaotao Jia, You Wang, Zhe Huang, Yue Zhang & Weisheng Zhao

  2. Fert Beijing Institute, BDBC and School of Computer Science and Engineering, Beihang University, Beijing, China

    Jianlei Yang

  3. The Department of Electrical and Computer Engineering, University of Alberta Edmonton, Alberta, Canada

    Yuanzhuo Qu, Bruce F. Cockburn & Jie Han

Authors
  1. Xiaotao Jia
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  2. You Wang
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  3. Zhe Huang
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  4. Yue Zhang
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  5. Jianlei Yang
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  6. Yuanzhuo Qu
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  7. Bruce F. Cockburn
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  8. Jie Han
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  9. Weisheng Zhao
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Corresponding authors

Correspondence to Yue Zhang or Jianlei Yang .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, McGill University, Montréal, QC, Canada

    Warren J. Gross

  2. ECE Department, University of Waterloo, Waterloo, ON, Canada

    Vincent C. Gaudet

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Jia, X. et al. (2019). Spintronic Solutions for Stochastic Computing. In: Gross, W., Gaudet, V. (eds) Stochastic Computing: Techniques and Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-03730-7_9

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  • DOI: https://doi.org/10.1007/978-3-030-03730-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03729-1

  • Online ISBN: 978-3-030-03730-7

  • eBook Packages: EngineeringEngineering (R0)

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