Harnessing Nonlinear Broadening Dynamics in Single-Mode Fibers for Neuromorphic Computing

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.