Driven semiconductor lasers for information processing
Mirko Goldmann is pursuing his PhD in the Nonlinear Photonics group at the Institute of Interdisciplinary Physics and Complex Systems (IFISC UIB-CSIC) in Spain. He completed his master's degree at TU Berlin with a thesis about “Deep delay-based reservoir computing”. Additionally, he is a member of the European Training Network POST-DIGITAL, which aims to develop analog photonic hardware for machine learning applications. Mirko’s research intersects nonlinear dynamics, photonics, and machine learning. His work primarily investigates photonic hardware for machine learning tasks such as time series prediction and image classification, particularly focusing on enhancing training capabilities.
(This talk is a rehearsal for an upcoming presentation at SPIE Photonics Europe)
Photonic systems, exhibiting multi-gigahertz bandwidth, facilitate data transmission at gigabit-per-second rates. While traditionally used in optical communication for data transfer, semiconductor lasers are now being explored for their potential in optical computation and signal processing. Injecting information into these lasers leads to nonlinear transformations and high-speed processing. Experimentally, a single semiconductor laser shows essential features for versatile computation, such as high-dimensional and nonlinear responses within sub-nanoseconds. To boost computational power, we study numerically the training of delay-coupled laser networks. The objective is, akin to training artificial neural networks, optimizing laser network's to improve performance and computational efficiency in challenging machine learning tasks. However, relying on offline optimization methods and physical models raises challenges due to device variability and limited system observability. Here, we propose evolutionary strategies to optimize physical systems without needing precise model knowledge, offering a promising approach for online system optimization.
Link: https://meet.google.com/txr-rhtw-fhc
Contact details:
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