An optical neural network using less than 1 photon per multiplication

Tianyu Wang (), Shi-Yuan Ma, Logan G. Wright, Tatsuhiro Onodera, Brian C. Richard and Peter L. McMahon ()
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Tianyu Wang: Cornell University
Shi-Yuan Ma: Cornell University
Logan G. Wright: Cornell University
Tatsuhiro Onodera: Cornell University
Brian C. Richard: Cornell University
Peter L. McMahon: Cornell University

Nature Communications, 2022, vol. 13, issue 1, 1-8

Abstract: 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.

Date: 2022
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Citations: View citations in EconPapers (9)

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DOI: 10.1038/s41467-021-27774-8

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