An optical neural network using less than 1 photon per multiplication
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Date
2022-01-10
Authors
Wang, Tianyu
Ma, Shi-Yuan
Wright, Logan G.
Onodera, Tatsuhiro
Richard, Brian C.
McMahon, Peter L.
Version
Published version
OA Version
Citation
T. Wang, S.-Y. Ma, L.G. Wright, T. Onodera, B.C. Richard, P.L. McMahon. 2022. "An optical neural network using less than 1 photon per multiplication." Nature Communications, Volume 13, Issue 1, pp.123-. https://doi.org/10.1038/s41467-021-27774-8
Abstract
Deep learning has become a widespread tool in both science and industry. However, continued progress is hampered by the rapid growth in energy costs of ever-larger deep neural networks. Optical neural networks provide a potential means to solve the energy-cost problem faced by deep learning. Here, we experimentally demonstrate an optical neural network based on optical dot products that achieves 99% accuracy on handwritten-digit classification using ~3.1 detected photons per weight multiplication and ~90% accuracy using ~0.66 photons (~2.5 × 10-19 J of optical energy) per weight multiplication. The fundamental principle enabling our sub-photon-per-multiplication demonstration-noise reduction from the accumulation of scalar multiplications in dot-product sums-is applicable to many different optical-neural-network architectures. Our work shows that optical neural networks can achieve accurate results using extremely low optical energies.
Description
License
© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.