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.
@article{arxiv.2305.14927,
title = {Scalable wavelength-multiplexing photonic reservoir computing},
author = {Rui-Qian Li and Yi-Wei Shen and Bao-De Lin and Jingyi Yu and Xuming He and Cheng Wang},
journal= {arXiv preprint arXiv:2305.14927},
year = {2023}
}