Scalable wavelength-multiplexing photonic reservoir computing

2023/05/24 by Rui-Qian Li, Li, Rui-Qian, Yi-Wei Shen +9
Computer Science · Engineering · #Neural Networks and Reservoir Computing #Optical Network Technologies #Photonic and Optical Devices

paper · pdf · doi:10.48550/arxiv.2305.14927

Abstract

Photonic reservoir computing (PRC) is a special hardware recurrent neural network, which is featured with fast training speed and low training cost. This work shows a wavelength-multiplexing PRC architecture, taking advantage of the numerous longitudinal modes in a Fabry-Perot semiconductor laser. These modes construct connected physical neurons in parallel, while an optical feedback loop provides interactive virtual neurons in series. We experimentally demonstrate a four-channel wavelength-multiplexing PRC, which runs four times faster than the single-channel case. It is proved that the multiplexing PRC exhibits superior performance on the task of signal equalization in an optical fiber communication link. Particularly, this scheme is highly scalable owing to the rich mode resources in Fabry-Perot lasers.

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