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
Book ChapterDOI
2D materials in nonlinear optics
TL;DR: In this paper, the authors summarize the NLO properties of typical 2D materials, including graphene, black phosphorus, topological insulators, transition metal chalcogenides, and hexagonal boron nitride, which will play an important role in OSP.
Journal ArticleDOI
Controlling chaotic itinerancy in laser dynamics for reinforcement learning
TL;DR: This study proposes a method for controlling the chaotic itinerancy in a multi-mode semiconductor laser to solve a machine learning task, known as the multi-armed bandit problem, which is fundamental to reinforcement learning.
Proceedings ArticleDOI
Photonic Neuromorphic Computing: Architectures, Technologies, and Training Models
Miltiadis Moralis-Pegios,Angelina Totovic,Apostolos Tsakyridis,George Giamougiannis,George Mourgias-Alexandris,George Dabos,Nikolaos Passalis,Manos Kirtas,Anastasios Tefas,Nikos Pleros +9 more
TL;DR: This work summarizes recent developments in neuromorphic photonics, including work and the advances it brings beyond the state-of-the-art demonstrators in terms of architectures, technologies, and training models for a synergistic hardware/software codesign approach.
Journal ArticleDOI
Analytical modeling of the static and dynamic response of thermally actuated optical waveguide circuits
Ciro Pentangelo,Ciro Pentangelo,Simone Atzeni,Simone Atzeni,Francesco Ceccarelli,Francesco Ceccarelli,Roberto Osellame,Roberto Osellame,Andrea Crespi,Andrea Crespi +9 more
TL;DR: In this article, an analytical model of the heat diffusion, adapted to the typical geometry of optical chips, is proposed to describe the static and dynamic response of thermo-optic phase shifters.
Journal ArticleDOI
Ultra-Broadband, Fabrication Tolerant Optical Coupler for Arbitrary Splitting Ratio Using Particle Swarm Optimization Algorithm
TL;DR: In this article, a design approach of multi-segment directional couplers with ultra-broadband flat spectra and benign fabrication tolerance on the silicon nitride platform 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.