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18 June 2024 In-fiber inference capabilities of femtosecond pulse spectral broadening in diverse nonlinear regimes
Mario Chemnitz, Mohammad Sobhi Saeed, Mehmet Müftüoglu, Bennet Fischer
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Proceedings Volume PC13017, Machine Learning in Photonics; PC130170S (2024) https://doi.org/10.1117/12.3022480
Event: SPIE Photonics Europe, 2024, Strasbourg, France
Abstract
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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Mario Chemnitz, Mohammad Sobhi Saeed, Mehmet Müftüoglu, and Bennet Fischer "In-fiber inference capabilities of femtosecond pulse spectral broadening in diverse nonlinear regimes", Proc. SPIE PC13017, Machine Learning in Photonics, PC130170S (18 June 2024); https://doi.org/10.1117/12.3022480
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KEYWORDS
Femtosecond phenomena
Femtosecond pulse shaping
Computer hardware
Education and training
Supercontinuum generation
Phase shift keying
Reservoir computing
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