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
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Journal ArticleDOI
Computational metrics and parameters of an injection-locked large area semiconductor laser for neural network computing [Invited]
TL;DR: In this paper , the performance of a scalable, fully parallel and autonomous photonic neural network based on large area vertical-cavity surface-emitting lasers (LA-VCSEL) is investigated.
Journal ArticleDOI
Analog nanophotonic computing going practical: silicon photonic deep learning engines for tiled optical matrix multiplication with dynamic precision
George Giamougiannis,Apostolos Tsakyridis,Miltiadis Moralis-Pegios,Christos Pappas,Manos Kirtas,Nikolaos Passalis,David Lazovsky,Anastasios Tefas,Nikos Pleros +8 more
TL;DR: In this article , the authors proposed and experimentally demonstrated a speed-optimized dynamic precision neural network inference via tiled matrix multiplication (TMM) on a low-radix silicon photonic processor.
Journal ArticleDOI
Deep Learning for Photonic Design and Analysis: Principles and Applications
TL;DR: The recent advances of deep learning for the photonic structure design and optical data analysis are reviewed, which is based on the two major learning paradigms of supervised learning and unsupervised learning.
Journal ArticleDOI
Neural Schrödinger Equation: Physical Law as Deep Neural Network
TL;DR: In this paper , a new family of neural networks based on the Schr\"{o}dinger equation (SE-NET) was proposed, where the trainable weights of the neural networks correspond to the physical quantities of the Schr''{o''dinger equations.
Peer ReviewDOI
Neuromorphic Computing Based on Wavelength-Division Multiplexing
Xingyuan Xu,Weiwei Han,Meng Peun Tan,Yang Sun,Yonghui Li,Jiayang Wu,Roberto Morandotti,Arnan Mitchell,Kun Xu,David J. Moss +9 more
TL;DR: In this paper , the authors present results for human image processing using an optical convolution accelerator operating at 11 Tera operations per second and discuss the open challenges and limitations of optical neural networks.
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