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NEUROMORPHIC COMPUTING

Memristors learn to play

Nature Electronics volume 2, pages 96–97 (2019)Cite this article

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A hybrid analogue–digital computing system based on memristive devices is capable of solving classic control problems with potentially a lower energy consumption and higher speed than fully digital systems.

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Fig. 1: The next game for memristors.

References

  1. Mnih, D. et al. Nature 518, 529–533 (2015).

    Article  Google Scholar 

  2. Silver, D. et al. Nature 550, 354–359 (2017).

    Article  Google Scholar 

  3. OpenAI et al. Preprint at https://arxiv.org/abs/1808.00177 (2019).

  4. Williams, G. et al. In 2017 IEEE Int. Conf. Robotics Autom. (ICRA) 1714–1721 (2017).

  5. Zoph, B. & Le, Q. V. In 2017 Int. Conf. Learn. Represent. (ICLR) https://openreview.net/pdf?id=r1Ue8Hcxg (ICLR, 2017).

  6. Silver, D. et al. Preprint at https://arxiv.org/abs/1712.01815 (2017).

  7. Prezioso, M. et al. Nature 521, 61–64 (2015).

    Article  Google Scholar 

  8. Wang, Z. et al. Nat. Electron. https://doi.org/10.1038/s41928-019-0221-6 (2019).

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

    Article  Google Scholar 

  10. Davies, M. et al. IEEE Micro 38, 82–99 (2018).

    Article  Google Scholar 

  11. Verzi, S. J. et al. Neural Comput. 30, 2660–2690 (2018).

    Article  MathSciNet  Google Scholar 

  12. Severa, W. et al. Nat. Mach. Intell. 1, 86–94 (2019).

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Center for Computing Research, Sandia National Laboratories, Albuquerque, NM, USA

    Sam Green & James B. Aimone

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  1. Sam Green
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  2. James B. Aimone
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Correspondence to James B. Aimone.

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Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, a wholly owned subsidiary of Honeywell International, for the US Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This article describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the US Department of Energy or the US government.

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Green, S., Aimone, J.B. Memristors learn to play. Nat Electron 2, 96–97 (2019). https://doi.org/10.1038/s41928-019-0224-3

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