Neuromorphic computing in Ginzburg-Landau Polariton-Lattice Systems
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, roboti...
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sg-ntu-dr.10356-1059352023-02-28T19:37:41Z Neuromorphic computing in Ginzburg-Landau Polariton-Lattice Systems Opala, Andrzej Ghosh, Sanjib Liew, Timothy C. H. Matuszewski, Michał School of Physical and Mathematical Sciences Neural Network Exciton Polariton DRNTU::Science::Physics 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. MOE (Min. of Education, S’pore) Published version 2019-06-18T02:39:30Z 2019-12-06T22:01:04Z 2019-06-18T02:39:30Z 2019-12-06T22:01:04Z 2019 Journal Article 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 https://hdl.handle.net/10356/105935 http://hdl.handle.net/10220/48789 10.1103/PhysRevApplied.11.064029 en Physical Review Applied https://doi.org/10.21979/N9/OZF3HV © 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). 10 p. application/pdf application/octet-stream |
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Neural Network Exciton Polariton DRNTU::Science::Physics Opala, Andrzej Ghosh, Sanjib Liew, Timothy C. H. Matuszewski, Michał Neuromorphic computing in Ginzburg-Landau Polariton-Lattice Systems |
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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. |
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School of Physical and Mathematical Sciences |
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School of Physical and Mathematical Sciences Opala, Andrzej Ghosh, Sanjib Liew, Timothy C. H. Matuszewski, Michał |
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Article |
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Opala, Andrzej Ghosh, Sanjib Liew, Timothy C. H. Matuszewski, Michał |
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Opala, Andrzej |
title |
Neuromorphic computing in Ginzburg-Landau Polariton-Lattice Systems |
title_short |
Neuromorphic computing in Ginzburg-Landau Polariton-Lattice Systems |
title_full |
Neuromorphic computing in Ginzburg-Landau Polariton-Lattice Systems |
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Neuromorphic computing in Ginzburg-Landau Polariton-Lattice Systems |
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Neuromorphic computing in Ginzburg-Landau Polariton-Lattice Systems |
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neuromorphic computing in ginzburg-landau polariton-lattice systems |
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2019 |
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https://hdl.handle.net/10356/105935 http://hdl.handle.net/10220/48789 https://doi.org/10.21979/N9/OZF3HV |
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