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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Journal ArticleDOI
Deep Learning Designs of Anisotropic Metasurfaces in Ultrawideband Based on Generative Adversarial Networks
TL;DR: A deep neural network model for inverse designs of anisotropic metasurfaces with full phase properties in ultrawideband is proposed, demonstrating that the reflection phases of the generated meta‐atoms have excellent agreements with the given targets, providing an efficient way in automatically designing metAsurfaces.
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Efficient Trainability of Linear Optical Modules in Quantum Optical Neural Networks
TL;DR: In this article, the authors show that coherent light in m modes can be generated efficiently if the total intensity scales sublinearly with m, and extend this result to cost functions based on homodyne, heterodyne or photon detection measurement statistics, and to noisy cost functions in the presence of attenuation.
Journal ArticleDOI
Photonic Integrated Reconfigurable Linear Processors as Neural Network Accelerators
Lorenzo De Marinis,Marco Cococcioni,Odile Liboiron-Ladouceur,Giampiero Contestabile,Piero Castoldi,Nicola Andriolli +5 more
TL;DR: The silicon-on-insulator processor outperforms the silicon nitride one in terms of footprint and energy efficiency and the lower extinction ratio of Mach–Zehnder elements in the latter platform limits their expressivity.
Journal ArticleDOI
Momentum-space imaging spectroscopy for the study of nanophotonic materials
Yiwen Zhang,Yiwen Zhang,Maoxiong Zhao,Jiajun Wang,Wenzhe Liu,Bo Wang,Hu Songting,Guopeng Lu,Ang Chen,Cui Jing,Weiyi Zhang,Chia Wei Hsu,Xiaohan Liu,Lei Shi,Haiwei Yin,Jian Zi +15 more
TL;DR: In this article, a momentum-space imaging spectroscopy (MSIS) system is presented, which can directly study the spectral information in momentum space, and the photonic dispersion can be captured in one shot with high energy and momentum resolution.
Journal ArticleDOI
Photonic machine learning with on-chip diffractive optics
Tingzhao Fu,Yubin Zang,Yuyao Huang,Zhenmin Du,Ho-Chao Shindian Huang,Chengyang Hu,Minghua Chen,Sigang Yang,Hongwei Chen +8 more
TL;DR: In this article , an on-chip diffractive optical neural network (DONN) based on a silicon-on-insulator platform is proposed to perform machine learning tasks with high integration and low power consumption characteristics.
References
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