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Low-cost stochastic number generator based on MRAM for stochastic computing

Published:31 May 2023Publication History

ABSTRACT

Stochastic computing (SC) can transform the major operations of neural network, i.e. multiply-and-accumulate (MAC), into AND and multiplexer, which drastically reduce the hardware occupation and energy consumption. This paper proposes a novel design of SC for highly energy-efficient computing which combines the features of low power and stochastic switching of magnetic random access memory (MRAM) and the intrinsic fault-tolerance and simple arithmetic operations of SC. A simplified circuit of stochastic number generater (SNG) based on MRAM device is proposed to transform the binary bitstream into stochastic bitstream. Compared with the conventional SNGs, the proposed SNG reduces considerably the design complexity and saves the energy consumption in consequence. Furthermore, the performance is investigated in terms of accuracy and hardware occupation to explore the design space.

References

  1. A. Ardakani, F. Leduc-Primeau, N. Onizawa, T. Hanyu, and W. J. Gross. Vlsi implementation of deep neural network using integral stochastic computing. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 25(10):2688--2699, Oct 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. V. Canals, A. Morro, A. Oliver, M. L. Alomar, and J. L. Rosselló. A new stochastic computing methodology for efficient neural network implementation. IEEE Transactions on Neural Networks and Learning Systems, 27(3):551--564, March 2016.Google ScholarGoogle ScholarCross RefCross Ref
  3. J. Yu, K. Kim, J. Lee, and K. Choi. Accurate and efficient stochastic computing hardware for convolutional neural networks. In 2017 IEEE International Conference on Computer Design (ICCD), pages 105--112, Nov 2017.Google ScholarGoogle ScholarCross RefCross Ref
  4. N. Onizawa, D. Katagiri, W. J. Gross, and T. Hanyu. Analog-to-stochastic converter using magnetic tunnel junction devices for vision chips. IEEE Transactions on Nanotechnology, 15(5):705--714, Sept 2016.Google ScholarGoogle ScholarCross RefCross Ref
  5. R. Venkatesan, S. Venkataramani, X. Fong, K. Roy, and A. Raghunathan. Spintastic: Spin-based stochastic logic for energy-efficient computing. In 2015 Design, Automation Test in Europe Conference Exhibition (DATE), pages 1575--1578, March 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. G. Srinivasan, A. Sengupta, and K. Roy. Magnetic tunnel junction enabled all-spin stochastic spiking neural network. In Design, Automation Test in Europe Conference Exhibition (DATE), 2017, pages 530--535, March 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. You Wang, Yue Zhang, Youguang Zhang, Weisheng Zhao, Hao Cai, and Lirida Naviner. Design space exploration of magnetic tunnel junction based stochastic computing in deep learning. In Proceedings of the 2018 on Great Lakes Symposium on VLSI, GLSVLSI '18, page 403--408, New York, NY, USA, 2018. Association for Computing Machinery.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Y. Wang, H. Cai, L. A. B. Naviner, J. O. Klein, Jianlei Yang, and W. Zhao. A novel circuit design of true random number generator using magnetic tunnel junction. In 2016 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH), pages 123--128, July 2016.Google ScholarGoogle Scholar
  9. A. Alaghi, Cheng Li, and J.P. Hayes. Stochastic circuits for real-time image-processing applications. In Design Automation Conference (DAC), 2013 50th ACM / EDAC / IEEE, pages 1--6, May 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. M. Hassan Najafi and David J. Lilja. High quality down-sampling for deterministic approaches to stochastic computing. IEEE Transactions on Emerging Topics in Computing, 9(1):7--14, 2021.Google ScholarGoogle ScholarCross RefCross Ref
  11. Weisheng Zhao and Guillaume Prenat. Spintronics-Based Computing. Springer, Berlin, Germany, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  12. S Ikeda, K Miura, H Yamamoto, K Mizunuma, H D Gan, M Endo, S Kanai, J Hayakawa, F Matsukura, and H Ohno. A perpendicular-anisotropy CoFeB-MgO magnetic tunnel junction. Nature Materials, 9:721--724, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  13. R. H. Koch, J. A. Katine, and J. Z. Sun. Time-resolved reversal of spin-transfer switching in a nanomagnet. Phys. Rev. Lett., 92:088302, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  14. D. C. Worledge, G. Hu, David W. Abraham, J. Z. Sun, P. L. Trouilloud, J. Nowak, S. Brown, M. C. Gaidis, E. J. O'Sullivan, and R. P. Robertazzi. Spin torque switching of perpendicular Ta/CoFeB/MgO-based magnetic tunnel junctions. Applied Physics Letters, 98(2):022501, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  15. R. Heindl, W. H. Rippard, S. E. Russek, M. R. Pufall, and A. B. Kos. Validity of the thermal activation model for spin-transfer torque switching in magnetic tunnel junctions. Journal of Applied Physics, 109(7):073910, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  16. H. Sato, M. Yamanouchi, S. Ikeda, S. Fukami, F. Matsukura, and H. Ohno. Perpendicular-anisotropy CoFeB-MgO magnetic tunnel junctions with a MgO/CoFeB/Ta/CoFeB/MgO recording structure. Applied Physics Letters, 101(2):022414, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  17. Y. Wang, H. Cai, L. A. d. B. Naviner, Y. Zhang, X. Zhao, E. Deng, J. O. Klein, and W. Zhao. Compact model of dielectric breakdown in spin-transfer torque magnetic tunnel junction. IEEE Transactions on Electron Devices, 63(4):1762--1767, April 2016.Google ScholarGoogle ScholarCross RefCross Ref
  18. H. Cai, Y. Wang, L. A. De Barros Naviner, and W. Zhao. Robust ultra-low power non-volatile logic-in-memory circuits in FD-SOI technology. IEEE Transactions on Circuits and Systems I: Regular Papers, 64(4):847--857, April 2017. Received 30 September 2022; accepted 30 October 2022Google ScholarGoogle ScholarCross RefCross Ref

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    • Published in

      cover image ACM Conferences
      NANOARCH '22: Proceedings of the 17th ACM International Symposium on Nanoscale Architectures
      December 2022
      140 pages
      ISBN:9781450399388
      DOI:10.1145/3565478

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      Publication History

      • Published: 31 May 2023

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      NANOARCH '22 Paper Acceptance Rate25of31submissions,81%Overall Acceptance Rate55of87submissions,63%
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