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Photonics for artificial intelligence and neuromorphic computing

Nature Photonics volume 15, pages 102–114 (2021)Cite this article

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Abstract

Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, particularly related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges.

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Fig. 1: Implementations of weights or photonic synapses.
Fig. 2: Photonic neurons incorporating weighting and nonlinearity.
Fig. 3: Excitable lasers and resonators for spiking.
Fig. 4: Photonic neural network implementations.
Fig. 5: Neuromorphic photonic processor architecture.

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Acknowledgements

B.J.S. acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC). T.F.d.L. and P.R.P. acknowledge support from the Office of Naval Research (ONR), Defense Advanced Research Projects Agency (DARPA) and National Science Foundation (NSF). We thank J. Shainline, P. Kuo and N. Sanford for editorial contributions.

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  1. These authors contributed equally: Bhavin J. Shastri, Alexander N. Tait.

Authors and Affiliations

  1. Department of Physics, Engineering Physics & Astronomy, Queen’s University, Kingston, Ontario, Canada

    Bhavin J. Shastri

  2. Department of Electrical Engineering, Princeton University, Princeton, NJ, USA

    Bhavin J. Shastri, Alexander N. Tait, T. Ferreira de Lima & Paul R. Prucnal

  3. Applied Physics Division, National Institute of Standards and Technology, Boulder, CO, USA

    Alexander N. Tait

  4. Institute of Physics and Center for Soft Nanoscience (SoN), University of Münster, Münster, Germany

    Wolfram H. P. Pernice

  5. Department of Materials, University of Oxford, Oxford, UK

    Harish Bhaskaran

  6. Department of Engineering, University of Exeter, Exeter, UK

    C. D. Wright

Authors
  1. Bhavin J. Shastri
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  2. Alexander N. Tait
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  3. T. Ferreira de Lima
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  4. Wolfram H. P. Pernice
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  5. Harish Bhaskaran
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  6. C. D. Wright
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  7. Paul R. Prucnal
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Corresponding authors

Correspondence to Bhavin J. Shastri or Alexander N. Tait.

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Peer review information Nature Photonics thanks Yichen Shen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Shastri, B.J., Tait, A.N., Ferreira de Lima, T. et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photonics 15, 102–114 (2021). https://doi.org/10.1038/s41566-020-00754-y

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