The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
An optical neural network using less than 1 photon per multiplication
Open accessAbstract
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.
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All-analog photoelectronic chip for high-speed vision tasks
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Single chip photonic deep neural network with accelerated training
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Metadata
- Authors
- Wang, T., Ma, S.-Y., Wright, L. G., Onodera, T., Richard, B. C., & McMahon, P. L.
- Venue
- Nature Communications
- Published
- 2022-01-10
- Volume
- 13
- Issue
- 1
- Pages
- 123-123
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W3159854664
- Cited by
- 268
- References
- 51
- FWCI
- 18.34
- Type
- journal article
- Language
- en
- Open access
- gold
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- Wang, T., Ma, S.-Y., Wright, L. G., Onodera, T., Richard, B. C., & McMahon, P. L. (2022). An optical neural network using less than 1 photon per multiplication. Nature Communications. https://doi.org/10.1038/s41467-021-27774-8
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