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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Journal ArticleDOI
Photonic integrated field-programmable disk array signal processor.
Weifeng Zhang,Jianping Yao +1 more
TL;DR: A scalable photonic field-programmable disk array (FPDA) signal processor that is field programmable using arrays of microdisk resonators is proposed.
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Recent Progress in Transistor-Based Optoelectronic Synapses: From Neuromorphic Computing to Artificial Sensory System
TL;DR: The recent progresses in transistor‐based optoelectronic synapses for artificial intelligent system are reviewed and their device architecture, neuromorphic operational mechanisms, manufacturing methodologies, and advanced applications for Artificial intelligent computing and visual perception systems are focused.
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Demonstration of chip-based coupled degenerate optical parametric oscillators for realizing a nanophotonic spin-glass.
Yoshitomo Okawachi,Mengjie Yu,Mengjie Yu,Jae K. Jang,Xingchen Ji,Yun Zhao,Bok Young Kim,Michal Lipson,Alexander L. Gaeta +8 more
TL;DR: The authors exploit χ nonlinearity in SiN to demonstrate on-chip phase-tunable coupling between two DOPO based Ising nodes, which can be deterministically achieved at a fast regeneration speed of 400 kHz with a large phase tolerance.
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Misalignment Resilient Diffractive Optical Networks
TL;DR: This work introduces and experimentally demonstrates a new training scheme that significantly increases the robustness of diffractive networks against 3D misalignments and fabrication tolerances in the physical implementation of a trained diffractive network.
Journal ArticleDOI
Emergent nonlinear phenomena in a driven dissipative photonic dimer
Alexey Tikan,Johann Riemensberger,K. Komagata,K. Komagata,Simon Hönl,Mikhail Churaev,Connor Skehan,Hairun Guo,Hairun Guo,Rui Ning Wang,Junqiu Liu,Paul Seidler,Tobias J. Kippenberg +12 more
TL;DR: In this article, a pair of photonic integrated Kerr micro-resonators (dimer) is shown to exhibit emergent nonlinear phenomena, such as spontaneous symmetry breaking and spontaneous symmetry hopping.
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.