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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Miniaturized Diffraction Grating Design and Processing for Deep Neural Network
Lidan Lu,Zhoumo Zeng,Lianqing Zhu,Qiankun Zhang,Bofei Zhu,Qi-feng Yao,Mingxing Yu,Haisha Niu,Mingli Dong,Guoshun Zhong +9 more
TL;DR: In this article, a long-wave infrared source with a wavelength of 10.6 um is used to establish an optical neural network transfer model using the Sommerfeld diffraction theory.
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Towards silicon photonic neural networks for artificial intelligence
TL;DR: A prototype of silicon photonic artificial intelligence processor for ultra-fast neural network computing is proposed and a detailed overview and a deeper understanding of this emerging field is given.
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A ferroelectric multilevel non-volatile photonic phase shifter
Jacqueline Geler-Kremer,Felix Eltes,Pascal Stark,David Stark,Daniele Caimi,Heinz Siegwart,Bert Jan Offrein,Jean Fompeyrine,Stefan Abel +8 more
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Non-Volatile Reconfigurable Silicon Photonics Based on Phase-Change Materials
TL;DR: In this paper , the authors review the recent progress in the field of nonvolatile reconfigurable silicon photonics based on phase change materials (PCMs) and discuss their operating wavelengths.
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Transient Tap Couplers for Wafer-Level Photonic Testing Based on Optical Phase Change Materials
Yifei Zhang,Qihang Zhang,Carlos Ríos,Mikhail Y. Shalaginov,Jeffrey B. Chou,Christopher Roberts,Paul Miller,Paul Robinson,Vladimir Liberman,Myungkoo Kang,Kathleen Richardson,Tian Gu,Steven A. Vitale,Juejun Hu +13 more
References
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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.