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:

ARTIFICIAL NEURAL NETWORKS

A role for analogue memory in AI hardware

Memristor-based chips could lead the way to fast, energy-efficient AI hardware accelerators.

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

Relevant articles

Open Access articles citing this article.

Access options

Buy this article

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

References

  1. Jouppi, N. P. et al. in Proc. 44th International Symposium on Computer Architecture 1–12 (2017).

  2. Sze, V., Chen, Y.-H., Yang, T.-J. & Emer, J. S. Proc. IEEE 105, 2295–2329 (2017).

    Article  Google Scholar 

  3. Li, C. et al. Nat. Mach. Intell. https://doi.org/10.1038/s42256-018-0001-4 (2018).

  4. LeCun, Y., Bengio, Y. & Hinton, G. Nature 521, 436–444 (2015).

    Article  Google Scholar 

  5. Han, S., Pool, J., Tran, J. & Dally, B. Proc. 28th International Conference on Neural Information Processing Systems 1, 1135–1143 (2015).

  6. Ambrogio, S. et al. Nature 558, 60–67 (2018).

    Article  Google Scholar 

  7. Nandakumar, S. R. et al. Preprint at https://arxiv.org/abs/1712.01192 (2017).

  8. Hu, M. et al. Adv. Mater. 30, 1705914 (2018).

    Article  Google Scholar 

  9. Shafiee, A. et al. in Proc. 43rd International Symposium on Computer Architecture 14–26 (2016).

  10. Gokmen, T. & Vlasov, Y. Front. Neurosci. 10, 333 (2016).

    Article  Google Scholar 

  11. Narayanan, P. et al. IBM J. Res. Dev. 61, 11 (2017).

    Article  Google Scholar 

  12. Tsai, H., Ambrogio, S., Narayanan, P., Shelby, R. M. & Burr, G. W. J. Phys. D 51, 283001 (2018).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Geoffrey W. Burr.

Ethics declarations

Competing interests

The author declares no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Burr, G.W. A role for analogue memory in AI hardware. Nat Mach Intell 1, 10–11 (2019). https://doi.org/10.1038/s42256-018-0007-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42256-018-0007-y

This article is cited by

Search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

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