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
Reads0
Chats0
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
Si Microring Resonator Crossbar Array for On-Chip Inference and Training of the Optical Neural Network
TL;DR: In this article , the authors proposed a Si programmable photonic integrated circuits (PIC) for optical domain matrix vector multiplication (MVM) and demonstrated a simple image classification task using this chip.
Journal ArticleDOI
Programmable low-threshold optical nonlinear activation functions for photonic neural networks.
TL;DR: Two types of programmable, low-threshold, optically controlled nonlinear activation functions, which are challenging to realize in photonic neural networks (PNNs), are experimentally demonstrated.
Proceedings ArticleDOI
Design Technology for Scalable and Robust Photonic Integrated Circuits: Invited Paper
TL;DR: This paper analyzes the scalability and noise robustness challenges facing photonic integrated circuits, for two representative PIC applications: logic computing and neural networks.
Journal ArticleDOI
Silicon photonic devices for scalable quantum information applications [Invited]
Lan-Tian Feng,Ming Fang Zhang,Jianwei Wang,Xiao-Qi Zhou,Xiaogang Qiang,Guang-Can Guo,Xi-Feng Ren +6 more
TL;DR: This paper reviews the relevant research results and state-of-the-art technologies on the silicon photonic chip for scalable quantum applications, and points out the challenges ahead and further research directions for on-chip scalable quantum information applications.
Journal ArticleDOI
Optimization of an H0 photonic crystal nanocavity using machine learning
TL;DR: In this article, a fully connected neural network (NN) was trained to obtain a coefficient of determination between predicted and calculated values of 0.977 for a photonic crystal nanocavity with randomly shifted holes.
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.