Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • News & Views
  • Published:

Machine learning

Creating stochastic neural networks with the help of probabilistic bits

Nature Electronics volume 6, pages 935–936 (2023)Cite this article

Subjects

Stochastic magnetic tunnel junctions can be used to create energy-efficient neural networks with confidence estimations.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Neural networks using stochastic MTJs as p-bits.

References

  1. Wulf, Wm. A. & McKee, S. A. ACM SIGARCH Comput. Architect. News 23, 20–24 (1995).

    Article  Google Scholar 

  2. Grollier, J. et al. Nat. Electron. 3, 360–370 (2020).

    Article  Google Scholar 

  3. Schuman, C. D. et al. Nat. Comput. Sci. 2, 10–19 (2022).

    Article  Google Scholar 

  4. Misra, S. et al. Adv. Mater. 35, 2204569 (2023).

    Article  Google Scholar 

  5. Liu, S. et al. IEEE J. Exploratory Solid State Comput. Dev. Circ. 8, 194–202 (2022).

    Google Scholar 

  6. Singh, N. S. et al. In Proc. 2023 IEEE Int. Electron Devices Meeting (in the press); https://ieee-iedm.org/program

  7. Safranski, C. et al. Nano Lett. 21, 2040–2045 (2021).

    Article  Google Scholar 

  8. Jiang, H., Kim, B., Guan, M. Y. & Gupta, M. In 32nd Conference on Neural Information Processing Systems (ACM, 2018); https://dl.acm.org/doi/10.5555/3327345.3327458

  9. Aadit, N. A. et al. Nat. Electron. 5, 460–468 (2022).

    Article  Google Scholar 

  10. Harabi, K.-E. et al. Nat. Electron. 6, 53–63 (2022).

    Google Scholar 

  11. Sebastian, A. et al. Nat. Commun. 13, 6139 (2022).

    Article  Google Scholar 

  12. Liu, S. et al. Front. Nanotechnol. 4, 1021943 (2022).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA

    Samuel Liu & Jean Anne C. Incorvia

Authors
  1. Samuel Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jean Anne C. Incorvia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jean Anne C. Incorvia.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, S., Incorvia, J.A.C. Creating stochastic neural networks with the help of probabilistic bits. Nat Electron 6, 935–936 (2023). https://doi.org/10.1038/s41928-023-01088-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41928-023-01088-7

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing