Optical neuromorphic computing marks a breakthrough over traditional digital computing by offering energy-efficient, fast, and parallel processing solutions while challenges remain in incorporating nonlinearity efficiently. Leveraging nonlinear wave dynamics in optical fibers as a computational resource may provide a solution. Our research demonstrates how femtosecond pulse propagation in optical fibers can emulate neural network inference, utilizing the high phase sensitivity of broadband light for creating nonlinear input-output mappings akin to Extreme Learning Machines (ELMs). Experimental results show high classification accuracies and low RMS errors in function regression, all at pico-joule pulse energy. This indicates our method's potential to lower energy consumption for inference tasks, complementing existing spatial-mode systems. We also investigated femtosecond pulses' nonlinear broadening effects - self-phase modulation and coherent soliton fission - demonstrating their distinct impacts on classification tasks and showcasing broadband frequency generation as a powerful, energy-efficient tool for next-generation computing.
Harnessing Nonlinear Broadening Dynamics in Single-Mode Fibers for Neuromorphic Computing / Chemnitz, M.; Fischer, B.; Zhu, Y.; Saeed, M. S.; Perron, N.; Alamgir, I.; Roztocki, P.; Maclellan, B.; Lauro, L. D.; Rimoldi, C.; Falk, T. H.; Morandotti, R.. - ELETTRONICO. - 13118:(2024), pp. 1-2. (Intervento presentato al convegno 2024 Emerging Topics in Artificial Intelligence, ETAI 2024 tenutosi a San Diego (USA) nel 18-23 August 2024) [10.1117/12.3028152].
Harnessing Nonlinear Broadening Dynamics in Single-Mode Fibers for Neuromorphic Computing
Rimoldi C.;
2024
Abstract
Optical neuromorphic computing marks a breakthrough over traditional digital computing by offering energy-efficient, fast, and parallel processing solutions while challenges remain in incorporating nonlinearity efficiently. Leveraging nonlinear wave dynamics in optical fibers as a computational resource may provide a solution. Our research demonstrates how femtosecond pulse propagation in optical fibers can emulate neural network inference, utilizing the high phase sensitivity of broadband light for creating nonlinear input-output mappings akin to Extreme Learning Machines (ELMs). Experimental results show high classification accuracies and low RMS errors in function regression, all at pico-joule pulse energy. This indicates our method's potential to lower energy consumption for inference tasks, complementing existing spatial-mode systems. We also investigated femtosecond pulses' nonlinear broadening effects - self-phase modulation and coherent soliton fission - demonstrating their distinct impacts on classification tasks and showcasing broadband frequency generation as a powerful, energy-efficient tool for next-generation computing.Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11583/2995724