Abstract
All-optical platforms for recurrent neural networks can offer higher computational speed and energy efficiency. To produce a major advance in comparison with currently available digital signal processing methods, the new system would need to have high bandwidth and operate both signal quadratures (power and phase). Here we propose a fiber echo state network analogue (FESNA) - the first optical technology that provides both high (beyond previous limits) bandwidth and dual-quadrature signal processing. We demonstrate applicability of the designed system for prediction tasks and for the mitigation of distortions in optical communication systems with multilevel dual-quadrature encoded signals.
Highlights
Machine learning, in particular neural networks (NNs), have recently attracted renewed and fastgrowing interest due to novel designs, advanced hardware implementations, and new applications
The surge of interest in recurrent neural networks (RNNs) is due to the broad spectrum of successful applications, which ranges from image analysis and classification, speech recognition, and language translation to more specific tasks like solving inverse imaging problems or signal processing in wireless and optical communications
Fiber-based NN was demonstrated recently with gigabyte per second speed [12], where memory was introduced by external electrical memory array - this imposed a limit on the design, as the learning could not be done in real time
Summary
In particular neural networks (NNs), have recently attracted renewed and fastgrowing interest due to novel designs, advanced hardware implementations, and new applications. The surge of interest in recurrent neural networks (RNNs) is due to the broad spectrum of successful applications, which ranges from image analysis and classification, speech recognition, and language translation to more specific tasks like solving inverse imaging problems or signal processing in wireless and optical communications. Fiber-based NN was demonstrated recently with gigabyte per second speed [12], where memory was introduced by external electrical memory array - this imposed a limit on the design, as the learning could not be done in real time
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