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
Photonic Reconfigurable Accelerators for Efficient Inference of CNNs With Mixed-Sized Tensors
TL;DR: This article presents a novel way of introducing reconfigurability in the MRR-based CNN accelerators, to enable dynamic maximization of the size compatibility between the accelerator hardware components and the CNN tensors that are processed using the hardware components.
Optical neural network quantum state tomography
TL;DR: In this paper , an optical neural network (ONN) for photonic polarization qubit quantum state tomography (QST) has been proposed, which can determine the phase parameter of the qubit state accurately.
Journal ArticleDOI
All‐Dielectric Metasurface Empowered Optical‐Electronic Hybrid Neural Networks
Geyang Qu,Guiyi Cai,Xinbo Sha,Qinmiao Chen,Jiaping Cheng,Yao Zhang,Jiecai Han,Qinghai Song,Shumin Xiao +8 more
TL;DR: In this article , a single metasurface-based optical-electronic hybrid neural network (OENN) was proposed and experimentally demonstrated, which is composed of a titanium dioxide (TiO2) and a fully connected electronic layer.
Journal ArticleDOI
Parallel and deep reservoir computing using semiconductor lasers with optical feedback
TL;DR: This study investigates parallel and deep configurations of delay-based all-optical reservoir computing using semiconductor lasers with optical feedback by combining multiple reservoirs to improve the performance of reservoir computing, finding that deep reservoirs are suitable for a chaotic time-series prediction task, whereas parallel reservoirs are suited for a nonlinear channel equalization task.
Journal ArticleDOI
Compact optical convolution processing unit based on multimode interference
X. Meng,Guo Zhen Zhang,Nuannuan Shi,Guang yi Li,José Azaña,José Capmany,Jianping Yao,Yichen Shen,Wei Liu,Ninghua Zhu,Ming Li +10 more
TL;DR: In this paper , a compact on-chip optical convolutional processing unit is fabricated on a low-loss silicon nitride platform to demonstrate its capability for large-scale integration, and three 2 × 2 correlated real-valued kernels are made of two multimode interference cells and four phase shifters to perform parallel convolution operations.
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.