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An optical neural network using less than 1 photon per multiplication.

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.

SUBMITTER: Wang T

PROVIDER: S-EPMC8748769 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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An optical neural network using less than 1 photon per multiplication.

Wang Tianyu T Ma Shi-Yuan SY Wright Logan G LG Onodera Tatsuhiro T Richard Brian C BC McMahon Peter L PL

Nature communications 20220110 1

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 multiplicatio ...[more]

PMID: 35013286

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