| Optical Neural Network Architecture for Deep Learning with Temporal Synthetic Dimension |
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| 作者姓名： | 彭擘 颜硕 成大立 俞丹英 刘展维 Vladislav V.Yakovlev 袁璐琦 陈险峰 |
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| 作者单位： | 1. State Key Laboratory of Advanced Optical Communication Systems and Networks,School of Physics and Astronomy, Shanghai Jiao Tong University;2. Ginzton Laboratory and Department of Electrical Engineering, Stanford University;3. Texas A&M University, College Station;5. Collaborative Innovation Center of Light Manipulation and Applications,Shandong Normal University |
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| 基金项目： | supported by the National Natural Science Foundation of China (Grant Nos. 12122407, 11974245, and 12192252)；;NIH (Grant Nos. 1R01GM127696-01 and 1R21GM142107-01)； |
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| 摘 要： | The physical concept of synthetic dimensions has recently been introduced into optics. The fundamental physics and applications are not yet fully understood, and this report explores an approach to optical neural networks using synthetic dimension in time domain, by theoretically proposing to utilize a single resonator network, where the arrival times of optical pulses are interconnected to construct a temporal synthetic dimension.The set of pulses in each roundtrip therefore provides the sites ...
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