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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Generalized Robust Training Scheme using Genetic Algorithm for Optical Neural Networks with Imprecise Components
Rui Shao,Gong Zhang,Xiao Gong +2 more
TL;DR: In this article , a two-step ex situ training scheme is proposed to configure phase shifts in a Mach-Zehnder-interferometer-based feedforward ONN, where a stochastic gradient descent algorithm followed by a genetic algorithm considering four types of practical imprecisions is employed.
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Hybrid training of optical neural networks
TL;DR: In this article , the authors demonstrate hybrid training of optical neural networks where the weight matrix is trained with neuron activation functions computed optically via forward propagation through the network, and they examine the efficacy of hybrid training with three different networks: an optical linear classifier, a hybrid opto-electronic network and a complex-valued optical network.
Journal ArticleDOI
Diffractive interconnects: all-optical permutation operation using diffractive networks
TL;DR: In this article , a diffractive optical network is proposed to all-optically perform permutation operations that can scale to hundreds of thousands of interconnections between an input and an output field-of-view using passive transmissive layers.
Journal ArticleDOI
Experimental quantum stochastic walks simulating associative memory of Hopfield neural networks
Hao Tang,Hao Tang,Zhen Feng,Zhen Feng,Ying-Han Wang,Peng-Cheng Lai,Chao-Yue Wang,Zhuo-Yang Ye,Cheng-Kai Wang,Zi-Yu Shi,Zi-Yu Shi,Tian-Yu Wang,Yuan Chen,Yuan Chen,Yuan Chen,Jun Gao,Jun Gao,Jun Gao,Xian-Min Jin,Xian-Min Jin +19 more
TL;DR: In this paper, a quantum stochastic walk is used to simulate associative memory in Hopfield neural networks, and a 3D photonic quantum chip is constructed to simulate the associative memories.
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Silicon photonic architecture for training deep neural networks with direct feedback alignment
TL;DR: In this article , a direct feedback alignment training algorithm was proposed to train neural networks using error feedback rather than error backpropagation, which can operate at speeds of trillions of multiply-accumulate (MAC) operations per second while consuming less than one picojoule per MAC operation.
References
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