Inference in artificial intelligence with deep optics and photonics

Abstract

Artificial intelligence tasks across numerous applications require accelerators for fast and low-power execution. Optical computing systems may be able to meet these domain-specific needs but, despite half a century of research, general-purpose optical computing systems have yet to mature into a practical technology. Artificial intelligence inference, however, especially for visual computing applications, may offer opportunities for inference based on optical and photonic systems. In this Perspective, we review recent work on optical computing for artificial intelligence applications and discuss its promise and challenges.

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Fig. 1: Timeline of artificial intelligence and related optical and photonic implementations.
Fig. 2: Overview of optical wave propagation.
Fig. 3: Illustration of an optical encoder–electronic decoder system.
Fig. 4: Overview of deep optics and photonics applications I.
Fig. 5: Overview of deep optics and photonics applications II.

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Acknowledgements

We thank E. Otte for help designing figures. G.W. was supported by an NSF CAREER Award (IIS 1553333), a Sloan Fellowship, by the KAUST Office of Sponsored Research through the Visual Computing Center CCF grant, and a PECASE by the US Army Research Office. A.O. was supported by an NSF ERC (PATHS-UP) grant. S.G. acknowledges funding from the European Research Council (ERC; H2020, SMARTIES-724473) and support from the Institut Universitaire de France. S.F. was supported by the US Air Force Office of Scientific Research (AFOSR) through the MURI project (grant no. FA9550-17-1-0002). D.E. and M.S. were in part supported by the US Army Research Office through the Institute for Soldier Nanotechnologies (grant no. W911NF-18-2-0048). D.E. also acknowledges support from an NSF EAGER programme. D.A.B.M. was supported by the Air Force Office of Scientific Research (award no. FA9550-17-1-0002). P.D. acknowledges discussions and a long-term collaboration with N. Farhat.

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G.W. conceived the idea, coordinated the writing process, wrote parts of the paper, and edited all sections. A.O., S.G., S.F.., D.E., M.S., C.D., D.A.B.M. and D.P. wrote parts of the paper and provided feedback on all other parts.

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Correspondence to Gordon Wetzstein.

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M.S. owns stocks of Lightelligence, Inc. S.G. owns stocks of LightOn. D.E. and D.A.B.M. own stocks in Lightmatter Inc. The other authors declare no competing financial interests.

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Peer review information Nature thanks Geoffrey W. Burr and Nathan Youngblood for their contribution to the peer review of this work.

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Wetzstein, G., Ozcan, A., Gigan, S. et al. Inference in artificial intelligence with deep optics and photonics. Nature 588, 39–47 (2020). https://doi.org/10.1038/s41586-020-2973-6

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