Harnessing nonlinear broadening dynamics in single-mode fibers for neuromorphic computing
Mario Chemnitz, Bennet Fischer, Yi Ming Zhu, Mohammad Sobhi Saeed, Nicolas Perron, Imtiaz Alamgir, Piotr Roztocki, Benjamin MacLellan, Luigi DiLauro, Cristina Rimoldi, Tiago Henrique Falk, Roberto Morandotti · 2024
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.