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 AND VIEWS

Evolution of circuits for machine learning

The fundamental machine-learning task of classification can be difficult to achieve directly in ordinary computing hardware. Unconventional silicon-based electrical circuits can be evolved to accomplish this task.
  1. Cyrus F. Hirjibehedin

    1. Cyrus F. Hirjibehedin is in the Quantum Information and Integrated Nanosystems group, Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, Massachusetts 02421, USA.

    View author publications

    You can also search for this author in PubMed  Google Scholar

Artificial intelligence (AI) has allowed computers to solve problems that were previously thought to be beyond their capabilities, from defeating the best human opponents in complex games1 to automating the identification of diseases2. There is therefore great interest in developing specialized circuits that can complete AI calculations faster and with lower energy consumption than can current devices. Writing in Nature, Chen et al.3 demonstrate an unconventional electrical circuit in silicon that can be evolved in situ to carry out basic machine-learning operations.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Nature 577, 320-321 (2020)

doi: https://doi.org/10.1038/d41586-020-00002-x

References

  1. Silver, D. et al. Nature 529, 484–489 (2016).

    Article  PubMed  Google Scholar 

  2. Liu, X. et al. Lancet Dig. Health 1, e271–e297 (2019).

    Article  Google Scholar 

  3. Chen, T. et al. Nature 577, 341–345 (2020).

    Article  Google Scholar 

  4. Merolla, P. A. et al. Science 345, 668–673 (2014).

    Article  PubMed  Google Scholar 

  5. Day, A., Wynn, A. & Golden, E. APS March Meet. 2020 abstr. L48.00008 (2020); go.nature.com/2ti4zt1

  6. Binnig, G., Rohrer, H., Gerber, Ch. & Weibel, E. Phys. Rev. Lett. 50, 120–123 (1983).

    Article  Google Scholar 

  7. Arute, F. et al. Nature 574, 505–510 (2019).

    Article  PubMed  Google Scholar 

  8. Bose, S. K. et al. Nature Nanotechnol. 10, 1048–1052 (2015).

    Article  PubMed  Google Scholar 

  9. Schofield, S. R. et al. Phys. Rev. Lett. 91, 136104 (2003).

    Article  PubMed  Google Scholar 

Download references

Subjects

Latest on:

Nature Careers

Jobs

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

Search

Advanced search

Quick links