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Inference in artificial intelligence with deep optics and photonics

Author

Listed:
  • 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)

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.

Suggested Citation

  • Gordon Wetzstein & Aydogan Ozcan & Sylvain Gigan & Shanhui Fan & Dirk Englund & Marin Soljačić & Cornelia Denz & David A. B. Miller & Demetri Psaltis, 2020. "Inference in artificial intelligence with deep optics and photonics," Nature, Nature, vol. 588(7836), pages 39-47, December.
    • Handle: RePEc:nat:nature:v:588:y:2020:i:7836:d:10.1038_s41586-020-2973-6
    DOI: 10.1038/s41586-020-2973-6
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    Cited by:

    1. Takuya Nakata & Sinan Chen & Masahide Nakamura, 2022. "Uni-Messe: Unified Rule-Based Message Delivery Service for Efficient Context-Aware Service Integration," Energies, MDPI, vol. 15(5), pages 1-18, February.
    2. Yuriy Leonidovich Zhukovskiy & Daria Evgenievna Batueva & Alexandra Dmitrievna Buldysko & Bernard Gil & Valeriia Vladimirovna Starshaia, 2021. "Fossil Energy in the Framework of Sustainable Development: Analysis of Prospects and Development of Forecast Scenarios," Energies, MDPI, vol. 14(17), pages 1-28, August.
    3. Anqi Ji & Jung-Hwan Song & Qitong Li & Fenghao Xu & Ching-Ting Tsai & Richard C. Tiberio & Bianxiao Cui & Philippe Lalanne & Pieter G. Kik & David A. B. Miller & Mark L. Brongersma, 2022. "Quantitative phase contrast imaging with a nonlocal angle-selective metasurface," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    4. Yaoyao Shi & Wei Sheng & Yangyang Fu & Youwen Liu, 2023. "Overlapping speckle correlation algorithm for high-resolution imaging and tracking of objects in unknown scattering media," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    5. Elena Goi & Steffen Schoenhardt & Min Gu, 2022. "Direct retrieval of Zernike-based pupil functions using integrated diffractive deep neural networks," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    6. H. H. Zhu & J. Zou & H. Zhang & Y. Z. Shi & S. B. Luo & N. Wang & H. Cai & L. X. Wan & B. Wang & X. D. Jiang & J. Thompson & X. S. Luo & X. H. Zhou & L. M. Xiao & W. Huang & L. Patrick & M. Gu & L. C., 2022. "Space-efficient optical computing with an integrated chip diffractive neural network," Nature Communications, Nature, vol. 13(1), pages 1-9, December.

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