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
Tunable Optical Properties of 2D Materials and Their Applications
Journal ArticleDOI
Tutorial: Integrated-photonic switching structures
TL;DR: In this paper, wave-guided 2 × 2 and N × M photonic switches are reviewed, including both broadband and narrowband resonant devices for the Si, InP, and AlN platforms.
Posted Content
ITO-based Electro-absorption Modulator for Photonic Neural Activation Function
Rubab Amin,Jonathan K. George,Shuai Sun,Thomas Ferreira de Lima,Alexander N. Tait,Jacob B. Khurgin,Mario Miscuglio,Bhavin J. Shastri,Paul R. Prucnal,Tarek El-Ghazawi,Volker J. Sorger +10 more
TL;DR: In this paper, an electro-absorption modulator based on an IndiumTin Oxide layer, whose dynamic range is used as nonlinear activation function of a photonic neuron, is presented.
Journal ArticleDOI
Reconfigurable all-optical nonlinear activation functions for neuromorphic photonics.
TL;DR: The device is programmable to generate various nonlinear activation functions, including sigmoid, radial-basis, clamped rectified linear unit, and softplus, with tunable thresholds, and can be flexibly programmed to optimize the performance of different neuromorphic tasks.
Journal ArticleDOI
Femtojoule per MAC Neuromorphic Photonics: An Energy and Technology Roadmap
TL;DR: It is revealed that silicon photonics can compete with the best-performing currently available digital electronic neural network engines, reaching TMAC/s/mm2 footprint- and sub-pJ/MAC energy efficiencies.
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