Parallel and deep reservoir computing based on frequency multiplexing

Alessandro Lupo; Marina Zajnulina; Serge Massar

doi:10.1117/12.2647351

15 March 2023 Parallel and deep reservoir computing based on frequency multiplexing

Alessandro Lupo, Marina Zajnulina, Serge Massar

Proceedings Volume 12438, AI and Optical Data Sciences IV; 124380B (2023) https://doi.org/10.1117/12.2647351
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

Reservoir Computers (RC) are brain-inspired algorithms that use partially untrained recurrent neural networks where only output connections are tuned. RCs can perform signal-analysis tasks such as distortion compensation. We recently demonstrated a photonic RC in which neurons are encoded in a frequency comb, untrained interconnections are realized by phase modulation, and trained output connections are realized by spectral filters. Here, we present a further development of this scheme in which the same substrate is used to implement two RCs simultaneously. The two RCs can either be used in parallel on different tasks, or in series, thereby implementing a “deep” RC.

Conference Presentation

Citation Download Citation

Alessandro Lupo, Marina Zajnulina, and Serge Massar "Parallel and deep reservoir computing based on frequency multiplexing", Proc. SPIE 12438, AI and Optical Data Sciences IV, 124380B (15 March 2023); https://doi.org/10.1117/12.2647351

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