Deep learning with coherent nanophotonic circuits
Yichen Shen,Nicholas C. Harris,Scott Skirlo,Dirk Englund,Marin Soljacic +4 more
- Vol. 11, Iss: 7, pp 441-446
TLDR
A new architecture for a fully optical neural network is demonstrated that enables a computational speed enhancement of at least two orders of magnitude and three order of magnitude in power efficiency over state-of-the-art electronics.Abstract:
Artificial Neural Networks have dramatically improved performance for many machine learning tasks. We demonstrate a new architecture for a fully optical neural network that enables a computational speed enhancement of at least two orders of magnitude and three orders of magnitude in power efficiency over state-of-the-art electronics.read more
Citations
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Journal ArticleDOI
Adjoint Method and Inverse Design for Nonlinear Nanophotonic Devices
TL;DR: This work presents an extension of the adjoint method to modeling nonlinear devices in the frequency domain, with the nonlinear response directly included in the gradient computation, to devise compact photonic switches in a Kerr nonlinear material.
Journal ArticleDOI
Artificial neural networks enabled by nanophotonics.
TL;DR: Research into emerging ANNs enabled by nanophtonics that harness photons’ ability to carry vast amounts of information that will help researchers develop artificial neural networks with uses including brain disease research and machine learning are reviewed.
Journal ArticleDOI
Machine learning and applications in ultrafast photonics
Goëry Genty,Lauri Salmela,John M. Dudley,Daniel Brunner,Alexey Kokhanovskiy,S. Kobtsev,Sergei K. Turitsyn,Sergei K. Turitsyn +7 more
TL;DR: A number of specific areas where the promise of machine learning in ultrafast photonics has already been realized are highlighted, including the design and operation of pulsed lasers, and the characterization and control of ultrafast propagation dynamics.
Journal ArticleDOI
Photonic Multiply-Accumulate Operations for Neural Networks
Mitchell A. Nahmias,Thomas Ferreira de Lima,Alexander N. Tait,Hsuan-Tung Peng,Bhavin J. Shastri,Paul R. Prucnal +5 more
TL;DR: This work describes the performance of photonic and electronic hardware underlying neural network models using multiply-accumulate operations, and investigates the limits of analog electronic crossbar arrays and on-chip photonic linear computing systems.
Journal ArticleDOI
Highlighting photonics: looking into the next decade
TL;DR: In this paper, the authors highlight a few emerging trends in photonics that they think are likely to have major impact at least in the upcoming decade, spanning from integrated quantum photonics and quantum computing, through topological/non-Hermitian photonics, to AI-empowered nanophotonics and photonic machine learning.
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