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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.
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Convolutional Deep Optical Learning Devices and Architectures
A coherent-optical implementation of a Rectifying Linear Unit (ReLu) as an interferometric phase-sensitive bidiectional switch along with convolutional layers using lenslet arrays andmultiplexed Fourier holograms allows the efficient implemention of Deep Neural Networks (DNN) in a self-aligning multilayer adaptive-holographic optical neural network.
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- Award ID(s):
- 1810508
- PAR ID:
- 10111565
- Date Published:
- Journal Name:
- Cognitive Computing 2018, Hannover
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
