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
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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
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Open-Access Silicon Photonics: Current Status and Emerging Initiatives
TL;DR: An overview of existing and upcoming commercial and noncommercial open-access silicon photonics technology platforms is presented and the diversity in these open- access platforms and their key differentiators are discussed.
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Controlling phonons and photons at the wavelength scale: integrated photonics meets integrated phononics
TL;DR: In this article, the state of the art in nanoscale electro-and optomechanical systems with a focus on scalable platforms such as silicon is summarized and perspectives on what these new systems may bring and what challenges they face in the coming years.
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Advances in on-chip photonic devices based on lithium niobate on insulator
TL;DR: In this paper, the authors present various on-chip LNOI devices categorized into nonlinear photonic and electro-optic tunable devices and photonic-integrated circuits.
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Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs)
Viraj Bangari,Bicky A. Marquez,Heidi B. Miller,Alexander N. Tait,Mitchell A. Nahmias,Thomas Ferreira de Lima,Hsuan-Tung Peng,Paul R. Prucnal,Bhavin J. Shastri +8 more
TL;DR: In this paper, the authors proposed a Digital Electronic and Analog Photonic (DEAP) architecture for convolutional neural networks (CNNs) that has potential to be 2.8 to 14 times faster while using almost 25% less energy than current state-of-the-art graphical processing units (GPUs).
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All-optical nonlinear activation function for photonic neural networks [Invited]
Mario Miscuglio,Armin Mehrabian,Zibo Hu,Shaimaa I. Azzam,Jonathan K. George,Alexander V. Kildishev,Matthew Pelton,Volker J. Sorger +7 more
TL;DR: In this paper, two independent approaches for implementing the optical perceptron's nonlinear activation function based on nanophotonic structures exhibiting i) induced transparency and ii) reverse saturated absorption are discussed.
References
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