Device‐System End‐to‐End Design of Photonic Neuromorphic Processor Using Reinforcement Learning

Author:

Tang Yingheng12,Zamani Princess Tara1,Chen Ruiyang1,Ma Jianzhu3,Qi Minghao2,Yu Cunxi1,Gao Weilu1ORCID

Affiliation:

1. Department of Electrical and Computer Engineering The University of Utah Salt Lake City UT 84112 USA

2. Elmore Family School of Electrical and Computer Engineering and Birck Nanotechnology Center Purdue University West Lafeyette IN 47907 USA

3. Institute for AI Industry Research Tsinghua University Beijing 10084 China

Funder

University of Utah

National Science Foundation

Publisher

Wiley

Subject

Condensed Matter Physics,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Trident edge coupler on thin-film lithium niobate for optimized coupling of octave-separated wavelengths for nonlinear applications;Optics Continuum;2024-07-15

2. AI-Enhanced Codesign of Neuromorphic Circuits;2023 IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS);2023-08-06

3. Universal Approach for Calibrating Large‐Scale Electronic and Photonic Crossbar Arrays;Advanced Intelligent Systems;2023-08

4. Diffractive Optical Neural Networks;Coded Optical Imaging;2023-08-01

5. LightRidge: An End-to-end Agile Design Framework for Diffractive Optical Neural Networks;Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 4;2023-03-25

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