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
Proceedings ArticleDOI
3D photo-responsive optical devices manufactured by advanced printing technologies
Adam Szukalski,Sureeporn Uttiya,Francesca D'Elia,Alberto Portone,Dario Pisignano,Luana Persano,Andrea Camposeo +6 more
TL;DR: In this article, the authors report on advanced additive manufacturing technologies, specifically designed to embed photo-responsive compounds in 3D optical devices, which can be controlled by external UV and visible light beams, with characteristic switching times in the range 1-10 s.
Proceedings ArticleDOI
GST integrated silicon photonics
TL;DR: In this paper, a phase change material (GSTM) was integrated with a Si ring resonator to demonstrate a quasi-continuous optical switch with extinction ratio as high as 33dB.
Journal ArticleDOI
All‐Dielectric Huygens’ Meta‐Waveguides for Resonant Integrated Photonics
Y. Denizhan Sirmaci,Ángela I. Barreda Gomez,Thomas Pertsch,Jens H. Schmid,Pavel Cheben,Isabelle Staude +5 more
TL;DR: In this paper , a novel silicon nanophotonic waveguide comprising a chain of resonantly forward scattering nanoparticles empowered by spectrally overlapping electric and magnetic dipolar Mie-type resonances is proposed and demonstrated.
All-Optical Nonlinear Activation Function Based on Germanium Silicon Hybrid Asymmetric Coupler
TL;DR: In this article, an all-optical implementation of a nonlinear activation function based on germanium silicon hybrid integration was proposed and demonstrated for optical neural networks (ONNs) to achieve more various functions.
Journal ArticleDOI
Silicon Photonic Phase Shifters and Their Applications: A Review
TL;DR: In this paper, a review of different types of silicon photonic phase shifters, including microelectro-mechanical systems (MEMS), thermo-optics, and free-carrier depletion types, highlighting the MEMS-based ones, is presented.
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.