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
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Posted Content
Backpropagation through nonlinear units for all-optical training of neural networks
TL;DR: It is found that the backward propagating gradients required to train the network can be approximated in a pump-probe scheme that requires only passive optical elements, and therefore provides a feasible path towards end-to-end optical training.
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Reservoir computing with solitons
TL;DR: This work proposes a versatile and robust soliton-based computing system using a discrete soliton chain as a reservoir and shows that sufficiently strong nonlinear dynamics allows it to perform accurate regression and classification tasks of non-linear separable datasets.
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Silicon microring synapses enable photonic deep learning beyond 9-bit precision
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Intelligent algorithms: new avenues for designing nanophotonic devices [Invited]
TL;DR: In this review, intelligent algorithms for designing nanophotonic devices are introduced from their concepts to their applications, including deep learning methods, the gradient-based inverse design method, swarm intelligence algorithms, individual inspired algorithms, and some other algorithms.
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Noise-enhanced spatial-photonic Ising machine
Davide Pierangeli,Davide Pierangeli,Giulia Marcucci,Giulia Marcucci,Daniel Brunner,Claudio Conti,Claudio Conti +6 more
TL;DR: It is demonstrated that an optimal noise level enhances the performance of spatial-photonic Ising machines on frustrated spin problems and may be crucial in developing novel hardware with optics-enabled parallel architecture for large-scale optimizations.
References
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