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
More filters
Journal ArticleDOI
Parallel convolution processing using an integrated photonic tensor core
Johannes Feldmann,Nathan Youngblood,Maxim Karpov,Helge Gehring,Xuan Li,Maik Stappers,Manuel Le Gallo,Xin Fu,Anton Lukashchuk,Arslan S. Raja,Junqiu Liu,David Wright,Abu Sebastian,Tobias J. Kippenberg,Wolfram H. P. Pernice,Harish Bhaskaran +15 more
TL;DR: The results indicate the potential of integrated photonics for parallel, fast, and efficient computational hardware in data-heavy AI applications such as autonomous driving, live video processing, and next-generation cloud computing services.
Journal ArticleDOI
Photonics for artificial intelligence and neuromorphic computing
Bhavin J. Shastri,Alexander N. Tait,Thomas Ferreira de Lima,Wolfram H. P. Pernice,Harish Bhaskaran,C. David Wright,Paul R. Prucnal +6 more
TL;DR: Recent advances in integrated photonic neuromorphic neuromorphic systems are reviewed, current and future challenges are discussed, and the advances in science and technology needed to meet those challenges are outlined.
Journal ArticleDOI
Deep learning for the design of photonic structures
TL;DR: Recent progress in deep-learning-based photonic design is reviewed by providing the historical background, algorithm fundamentals and key applications, with the emphasis on various model architectures for specific photonic tasks.
Journal ArticleDOI
Ultrafast machine vision with 2D material neural network image sensors
Lukas Mennel,Joanna Symonowicz,Stefan Wachter,Dmitry K. Polyushkin,Aday J. Molina-Mendoza,Thomas Mueller +5 more
TL;DR: It is demonstrated that an image sensor can itself constitute an ANN that can simultaneously sense and process optical images without latency, and is trained to classify and encode images with high throughput, acting as an artificial neural network.
Journal ArticleDOI
Inference in artificial intelligence with deep optics and photonics.
Gordon Wetzstein,Aydogan Ozcan,Sylvain Gigan,Shanhui Fan,Dirk Englund,Marin Soljacic,Cornelia Denz,David A. B. Miller,Demetri Psaltis +8 more
TL;DR: Recent work on optical computing for artificial intelligence applications is reviewed and its promise and challenges are discussed.
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.