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
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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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Robust Architecture-Agnostic and Noise Resilient Training of Photonic Deep Learning Models
Manos Kirtas,Nikolaos Passalis,George Mourgias-Alexandris,George Dabos,Nikos Pleros,Anastasios Tefas +5 more
TL;DR: In this paper , the authors propose a novel training method for photonic neuromorphic architectures that is capable of taking into account a wide range of limitations of the actual hardware, including noise sources and easily saturated activation mechanisms.
Journal ArticleDOI
Heterogeneously integrated III–V-on-Si microring resonators: a building block for programmable photonic integrated circuits
TL;DR: In this paper, the authors proposed and demonstrated proof-of-concept experiments of a heterogeneously integrated III-V-on-Si microring resonator (MRR) as such a versatile building block.
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Scattering statistics in nonlinear wave chaotic systems.
TL;DR: Researchers systematically studied how the key components in the RCM are affected by this nonlinear port, including the radiation impedance, short ray orbit corrections, and statistical properties, and developed a quantitative understanding of the statistical scattering properties of a semi-classical wave chaotic system with a nonlinear coupling channel.
Journal ArticleDOI
Strategies for training optical neural networks
TL;DR: In this paper , three classes of training strategies for optical neural networks (ONNs) have been designed: fine-tuning, backpropagation, and hybrid in-silico-in situ algorithm.
Proceedings ArticleDOI
Neuro-MMI: A Hybrid Photonic-Electronic Machine Learning Platform
TL;DR: A hybrid electronic-photonic feedforward neural network which exploits interference patterns in a Multimode Interference coupler (neuro-MMI) to serve as a blueprint for a class of high-performance neuromorphic networks that can solve cognitive tasks.
References
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