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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Proceedings ArticleDOI
Programmable Nanophotonics for Computation
TL;DR: This work towards realizing photonic matrix processors is discussed, which can serve as a particularly attractive computing platform for specific kinds of problems, including machine learning.
Journal ArticleDOI
A multichannel optical computing architecture for advanced machine vision
TL;DR: In this article , a multichannel optical neural network architecture for a universal multiple-input multiple-channel optical computing based on a novel projection-interference-prediction framework where the inter-and intra-channel connections are mapped to optical interference and diffraction is presented.
Smart sensors using artificial intelligence for on-detector electronics and ASICs
Gabriella Carini,Grzegorz Deptuch,Jennet Dickinson,Dionisio Doering,Angelo Dragone,Farah Fahim,Philip Harris,Ryan Herbst,Christian Herwig,Jin-zhi Huang,Soumyajit Mandal,Cristina Mantilla Suarez,Allison Mccarn Deiana,Sandeep Miryala,F. M. Newcomer,Benjamin Parpillon,Veljko Radeka,Dylan Rankin,Yihui Ren,Lorenzo Rota,L. L. Ruckman,Nhan T. Tran +21 more
TL;DR: The motivations and potential applications for on-detector AI, and a number of areas of opportunity where machine learning techniques, codesign workflows, and future microelectronics technologies which will accelerate design, performance, and implementations for next generation experiments are discussed.
Journal ArticleDOI
Photonics-enabled spiking timing-dependent convolutional neural network for real-time image classification.
TL;DR: A photonics-enabled spiking timing-dependent convolutional neural network is proposed by manipulating photonics multidimensional parameters in terms of wavelength, temporal and spatial, which breaks the traditional CNN architecture mapping from a spatially parallel to a time-dependent series structure.
Peer ReviewDOI
Silicon-based optoelectronics for general-purpose matrix computation: a review
Pengfei Xu,Zhiping Zhou +1 more
TL;DR: It is believed that silicon-based optoelectronics is a promising and comprehensive platform for disruptively improving general-purpose matrix computation performance in the post-Moore’s law era.
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.