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Title: Neuromorphic computing in Ginzburg-Landau Polariton-Lattice Systems
Authors: Opala, Andrzej
Ghosh, Sanjib
Liew, Timothy C. H.
Matuszewski, Michał
Keywords: Neural Network
Exciton Polariton
Issue Date: 2019
Source: Opala, A., Ghosh, S., Liew, T. C. H., & Matuszewski, M. (2019). Neuromorphic computing in Ginzburg-Landau Polariton-Lattice Systems. Physical Review Applied, 11(6). doi:10.1103/PhysRevApplied.11.064029
Series/Report no.: Physical Review Applied
Abstract: The availability of large amounts of data and the necessity of processing it efficiently have led to the rapid development of machine-learning techniques. To name a few examples, artificial-neural-network architectures are commonly used for financial forecasting, speech and image recognition, robotics, medicine, and even research. Direct hardware for neural networks is highly sought for overcoming the von Neumann bottleneck of software implementations. Reservoir computing (RC) is a recent and increasingly popular bio-inspired computing scheme that holds promise for efficient temporal information processing. We demonstrate the applicability and performance of RC in a general complex Ginzburg-Landau lattice model, which adequately describes the dynamics of a wide class of systems, including coherent photonic devices. In particular, we propose that the concept can be readily applied in exciton-polariton lattices, which are characterised by unprecedented photonic nonlinearity, opening the way to signal processing at rates of the order of 1 Tbit s−1.
DOI: 10.1103/PhysRevApplied.11.064029
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Rights: © 2019 American Physical Society (APS). All rights reserved. This paper was published in Physical Review Applied and is made available with permission of American Physical Society (APS).
Fulltext Permission: open
Fulltext Availability: With Fulltext
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