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PHOTONIC ARTIFICIAL INTELLIGENCE

Competitive photonic neural networks

Nature Photonics volume 15, pages 323–324 (2021)Cite this article

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Photonics offers high hopes for next-generation neural network processors. Now it has been shown that even entirely using off-the-shelf photonics allows surpassing speed and energy efficiency of cutting-edge GPUs.

Neural networks (NNs) are a neuro-inspired artificial intelligence concept that realizes computation based on the collective response of nonlinear elements (the neurons). Through this NNs mimic one of the most elementary aspects of computing in biological brains. A particular information processing task is ‘programmed’ by adjusting the network’s topology, that is, learning, usually on the basis of statistical optimization leveraging examples. NNs can learn to tell cats from dogs — or to carry out much more serious and useful feats and represent one of computing’s most exciting recent developments.

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Fig. 1: Optical neural network computing.

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Authors and Affiliations

  1. Optics Department, FEMTO-ST, UMR CNRS 6174, Univ. Bourgogne Franche-Comté, Besançon, France

    Daniel Brunner

  2. Optics Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Demetri Psaltis

Authors
  1. Daniel Brunner
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  2. Demetri Psaltis
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Correspondence to Daniel Brunner.

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The authors declare no competing interests.

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Brunner, D., Psaltis, D. Competitive photonic neural networks. Nat. Photonics 15, 323–324 (2021). https://doi.org/10.1038/s41566-021-00803-0

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