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
Highly parallel simulation and optimization of photonic circuits in time and frequency domain based on the deep-learning framework PyTorch
TL;DR: A new method for performing photonic circuit simulations based on the scatter matrix formalism, which allows for highly parallel simulation of large photonic circuits on graphical processing units in time and frequency domain while all parameters of each individual component can easily be optimized with well-established machine learning algorithms such as backpropagation.
Journal ArticleDOI
Intelligent meta-imagers: From compressed to learned sensing
TL;DR: In this article , the authors comprehensively review the evolution of computational meta-imaging from the earliest frequency-diverse compressive systems to modern programmable intelligent meta-implants.
Journal ArticleDOI
Scalable spin-glass optical simulator
Davide Pierangeli,Davide Pierangeli,Mushegh Rafayelyan,Claudio Conti,Claudio Conti,Sylvain Gigan +5 more
TL;DR: This work proposes and realizes an optical scalable spin-glass simulator based on spatial light modulation and multiple light scattering and optically accelerate the computation of the ground state of large spin networks with all-to-all random couplings, demonstrating optical advantage over conventional computing.
Proceedings ArticleDOI
Countering variations and thermal effects for accurate optical neural networks
Ying Zhu,Grace Li Zhang,Bing Li,Xunzhao Yin,Cheng Zhuo,Huaxi Gu,Tsung-Yi Ho,Ulf Schlichtmann +7 more
TL;DR: A framework to calibrate process variations and counter thermal effects by power compensation is proposed so that ONNs can achieve an inference accuracy similar to the accuracy after software training while providing their high bandwidth in neuromorphic computing.
Journal ArticleDOI
Optical patching scheme for optical convolutional neural networks based on wavelength-division multiplexing and optical delay lines
Shaofu Xu,Jing Wang,Weiwen Zou +2 more
TL;DR: An optical patching scheme is experimentally demonstrated to release the burden of electronic data processing and to cut down the scale of the input modulator array for optical CNNs.
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.