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
Citations
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Proceedings ArticleDOI
PIXEL: Photonic Neural Network Accelerator
TL;DR: A proposed PIXEL - Photonic Neural Network Accelerator that efficiently implements the fundamental operation in neural computation, namely the multiply and accumulate (MAC) functionality using photonic components such as microring resonators and Mach-Zehnder interferometer.
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Waveguide Engineering of Graphene Optoelectronics—Modulators and Polarizers
TL;DR: In this article, the authors theoretically demonstrate that by altering the dimension design of graphene-laminated silicon waveguides, the phase, amplitude, and polarization of the fundamental propagating modes can all be effectively tailored under different bias voltages.
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Self-calibrating programmable photonic integrated circuits
Xingyuan Xu,Guanghui Ren,Timothy Feleppa,Xu Feng Liu,Andreas Boes,Arnan Mitchell,Arthur J. Lowery +6 more
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A universal fully reconfigurable 12-mode quantum photonic processor
Caterina Taballione,Reinier van der Meer,Hendrik Joannes Snijders,Peter Hooijschuur,Jörn P. Epping,Michiel de Goede,Ben Kassenberg,Pim Venderbosch,Chris Toebes,Hans van den Vlekkert,Pepijn W. H. Pinkse,Jelmer J. Renema +11 more
TL;DR: In this paper, a 12-mode fully tunable linear interferometer with all-to-all mode coupling based on stoichiometric silicon nitride waveguides is presented.
Journal ArticleDOI
Programmable Photonics: An Opportunity for an Accessible Large-Volume PIC Ecosystem
Wim Bogaerts,Abdul Rahim +1 more
TL;DR: This work looks at the opportunities presented by the new concepts of generic programmable photonic integrated circuits (PIC) to deploy photonics on a larger scale, and makes a qualitative analysis of the possible application spaces where generic PICs can play an enabling role.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Journal ArticleDOI
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
Journal ArticleDOI
Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
Journal ArticleDOI
Deep learning in neural networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.