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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Main Authors: Opala, Andrzej, Ghosh, Sanjib, Liew, Timothy C. H., Matuszewski, Michał
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2019
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Online Access:https://hdl.handle.net/10356/105935
http://hdl.handle.net/10220/48789
https://doi.org/10.21979/N9/OZF3HV
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Institution: Nanyang Technological University
Language: English
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spelling 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
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Neural Network
Exciton Polariton
DRNTU::Science::Physics
spellingShingle 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
description 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.
author2 School of Physical and Mathematical Sciences
author_facet School of Physical and Mathematical Sciences
Opala, Andrzej
Ghosh, Sanjib
Liew, Timothy C. H.
Matuszewski, Michał
format Article
author Opala, Andrzej
Ghosh, Sanjib
Liew, Timothy C. H.
Matuszewski, Michał
author_sort 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
title_fullStr Neuromorphic computing in Ginzburg-Landau Polariton-Lattice Systems
title_full_unstemmed Neuromorphic computing in Ginzburg-Landau Polariton-Lattice Systems
title_sort neuromorphic computing in ginzburg-landau polariton-lattice systems
publishDate 2019
url https://hdl.handle.net/10356/105935
http://hdl.handle.net/10220/48789
https://doi.org/10.21979/N9/OZF3HV
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