Inference in artificial intelligence with deep optics and photonics

Gordon Wetzstein (), Aydogan Ozcan, Sylvain Gigan, Shanhui Fan, Dirk Englund, Marin Soljačić, Cornelia Denz, David A. B. Miller and Demetri Psaltis
Additional contact information
Gordon Wetzstein: Stanford University
Aydogan Ozcan: University of California, Los Angeles
Sylvain Gigan: Laboratoire Kastler Brossel, Sorbonne Université, École Normale Supérieure, Collège de France
Shanhui Fan: Stanford University
Dirk Englund: Massachusetts Institute of Technology
Marin Soljačić: Massachusetts Institute of Technology
Cornelia Denz: University of Münster
David A. B. Miller: Stanford University
Demetri Psaltis: École Polytechnique Fédérale de Lausanne

Nature, 2020, vol. 588, issue 7836, 39-47

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41586-020-2973-6

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