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.
Our research in neuromorphic computing leverages nonlinear optical dynamics to emulate neural network functionalities. In our experiments, we explore supercontinuum generation and other complex wave dynamics for information processing in the optical domain. Utilizing spectral-domain phase modulation and nonlinear femtosecond pulse broadening in multiple nonlinear fibers, we demonstrate effective data encoding and processing followed by a read-out layer training, akin to Extreme Learning Machines. Our benchmarks on diverse datasets showcase the scalability and inference capabilities of our system, and the distinct performance differences of two nonlinear domains, i.e. self-phase modulation and soliton fission. This work opens new avenues in quantifying physics-based analog computing platforms, suggesting implications for green computing, Big Data communications, and intelligent diagnostics.
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