Recurrent neural networks implemented through spatiotemporal light propagation in optical fibers
Dilem E\c{s}lik, Bahad{\i}r Utku Kesgin, U\u{g}ur Te\u{g}in

TL;DR
This paper demonstrates that multimode optical fibers can naturally implement recurrent neural network computations through passive light propagation, enabling energy-efficient temporal processing for various AI tasks without electronic feedback.
Contribution
It introduces a novel physical implementation of recurrent neural networks using spatiotemporal light dynamics in optical fibers, eliminating the need for trainable electronic components.
Findings
Achieved competitive performance on temporal tasks like time-series forecasting and action recognition.
Demonstrated energy-efficient processing leveraging intrinsic optical nonlinearities.
Showed that physical wave dynamics can serve as a basis for recurrent computation.
Abstract
Recurrent neural networks excel at temporal tasks and video processing but require energy-intensive sequential memory operations. We demonstrate that multimode optical fibers naturally implement spatiotemporal recurrent computation through passive light propagation. Video frames are encoded onto separate optical beams with controlled time delays; these beams combine and recirculate through a fiber loop where interference and nonlinear propagation generate high-dimensional states encoding both current inputs and fading memory. Remarkably, the entire optical system remains fixed with no trainable parameters or electronic feedback, yet this single physical configuration achieves competitive performance across diverse temporal and spatiotemporal learning tasks: chaotic time-series forecasting, human action recognition, steering angle prediction, and surgical skill assessment. Our results…
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Random lasers and scattering media · Advanced Fiber Laser Technologies
