11 TOPS photonic convolutional accelerator for optical neural networks

Convolutional neural networks, inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to provide greatly reduced parametric complexity and to enhance the accuracy of prediction. They are of great inte...

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Published in	Nature (London) Vol. 589; no. 7840; pp. 44 - 51
Main Authors	Xu, Xingyuan, Tan, Mengxi, Corcoran, Bill, Wu, Jiayang, Boes, Andreas, Nguyen, Thach G., Chu, Sai T., Little, Brent E., Hicks, Damien G., Morandotti, Roberto, Mitchell, Arnan, Moss, David J.
Format	Journal Article
Language	English
Published	London Nature Publishing Group UK 07.01.2021 Nature Publishing Group
Subjects	639/166/987 639/624/1111/1112 Accuracy Artificial neural networks Bands Bandwidths Cognitive tasks Complexity Computer vision Feature extraction Handwriting recognition Humanities and Social Sciences Learning algorithms Machine learning Medical imaging multidisciplinary Neural networks Object recognition Optical communication Pattern recognition Power Science Science (multidisciplinary) Speech recognition Temporal lobe Visual cortex Voice recognition
Online Access	Get full text
ISSN	0028-0836 1476-4687 1476-4687
DOI	10.1038/s41586-020-03063-0

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Abstract	Convolutional neural networks, inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to provide greatly reduced parametric complexity and to enhance the accuracy of prediction. They are of great interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis 1 – 7 . Optical neural networks offer the promise of dramatically accelerating computing speed using the broad optical bandwidths available. Here we demonstrate a universal optical vector convolutional accelerator operating at more than ten TOPS (trillions (10 12 ) of operations per second, or tera-ops per second), generating convolutions of images with 250,000 pixels—sufficiently large for facial image recognition. We use the same hardware to sequentially form an optical convolutional neural network with ten output neurons, achieving successful recognition of handwritten digit images at 88 per cent accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as autonomous vehicles and real-time video recognition. An optical vector convolutional accelerator operating at more than ten trillion operations per second is used to create an optical convolutional neural network that can successfully recognize handwritten digit images with 88 per cent accuracy.
AbstractList	Convolutional neural networks, inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to provide greatly reduced parametric complexity and to enhance the accuracy of prediction. They are of great interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis 1 – 7 . Optical neural networks offer the promise of dramatically accelerating computing speed using the broad optical bandwidths available. Here we demonstrate a universal optical vector convolutional accelerator operating at more than ten TOPS (trillions (10 12 ) of operations per second, or tera-ops per second), generating convolutions of images with 250,000 pixels—sufficiently large for facial image recognition. We use the same hardware to sequentially form an optical convolutional neural network with ten output neurons, achieving successful recognition of handwritten digit images at 88 per cent accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as autonomous vehicles and real-time video recognition. An optical vector convolutional accelerator operating at more than ten trillion operations per second is used to create an optical convolutional neural network that can successfully recognize handwritten digit images with 88 per cent accuracy. Convolutional neural networks, inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to provide greatly reduced parametric complexity and to enhance the accuracy of prediction. They are of great interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis . Optical neural networks offer the promise of dramatically accelerating computing speed using the broad optical bandwidths available. Here we demonstrate a universal optical vector convolutional accelerator operating at more than ten TOPS (trillions (10 ) of operations per second, or tera-ops per second), generating convolutions of images with 250,000 pixels-sufficiently large for facial image recognition. We use the same hardware to sequentially form an optical convolutional neural network with ten output neurons, achieving successful recognition of handwritten digit images at 88 per cent accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as autonomous vehicles and real-time video recognition. Convolutional neural networks, inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to provide greatly reduced parametric complexity and to enhance the accuracy of prediction. They are of great interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis. Optical neural networks offer the promise of dramatically accelerating computing speed using the broad optical bandwidths available. Here we demonstrate a universal optical vector convolutional accelerator operating at more than ten TOPS (trillions (10) of operations per second, or tera-ops per second), generating convolutions of images with 250,000 pixels-sufficiently large for facial image recognition. We use the same hardware to sequentially form an optical convolutional neural network with ten output neurons, achieving successful recognition of handwritten digit images at 88 per cent accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as autonomous vehicles and real-time video recognition. Convolutional neural networks, inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to provide greatly reduced parametric complexity and to enhance the accuracy of prediction. They are of great interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis1-7. Optical neural networks offer the promise of dramatically accelerating computing speed using the broad optical bandwidths available. Here we demonstrate a universal optical vector convolutional accelerator operating at more than ten TOPS (trillions (1012) of operations per second, or tera-ops per second), generating convolutions of images with 250,000 pixels-sufficiently large for facial image recognition. We use the same hardware to sequentially form an optical convolutional neural network with ten output neurons, achieving successful recognition of handwritten digit images at 88 per cent accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as autonomous vehicles and real-time video recognition.Convolutional neural networks, inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to provide greatly reduced parametric complexity and to enhance the accuracy of prediction. They are of great interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis1-7. Optical neural networks offer the promise of dramatically accelerating computing speed using the broad optical bandwidths available. Here we demonstrate a universal optical vector convolutional accelerator operating at more than ten TOPS (trillions (1012) of operations per second, or tera-ops per second), generating convolutions of images with 250,000 pixels-sufficiently large for facial image recognition. We use the same hardware to sequentially form an optical convolutional neural network with ten output neurons, achieving successful recognition of handwritten digit images at 88 per cent accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as autonomous vehicles and real-time video recognition.
Author	Mitchell, Arnan Morandotti, Roberto Xu, Xingyuan Chu, Sai T. Corcoran, Bill Wu, Jiayang Hicks, Damien G. Moss, David J. Boes, Andreas Little, Brent E. Tan, Mengxi Nguyen, Thach G.
Author_xml	– sequence: 1 givenname: Xingyuan orcidid: 0000-0002-8190-4700 surname: Xu fullname: Xu, Xingyuan organization: Optical Sciences Centre, Swinburne University of Technology, Electro-Photonics Laboratory, Department of Electrical and Computer Systems Engineering, Monash University – sequence: 2 givenname: Mengxi surname: Tan fullname: Tan, Mengxi organization: Optical Sciences Centre, Swinburne University of Technology – sequence: 3 givenname: Bill orcidid: 0000-0001-8653-3999 surname: Corcoran fullname: Corcoran, Bill organization: Department of Electrical and Computer Systems Engineering, Monash University – sequence: 4 givenname: Jiayang surname: Wu fullname: Wu, Jiayang organization: Optical Sciences Centre, Swinburne University of Technology – sequence: 5 givenname: Andreas orcidid: 0000-0001-8443-3396 surname: Boes fullname: Boes, Andreas organization: School of Engineering, RMIT University – sequence: 6 givenname: Thach G. surname: Nguyen fullname: Nguyen, Thach G. organization: School of Engineering, RMIT University – sequence: 7 givenname: Sai T. orcidid: 0000-0001-8263-8507 surname: Chu fullname: Chu, Sai T. organization: Department of Physics, City University of Hong Kong – sequence: 8 givenname: Brent E. surname: Little fullname: Little, Brent E. organization: Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences – sequence: 9 givenname: Damien G. orcidid: 0000-0001-8322-9983 surname: Hicks fullname: Hicks, Damien G. organization: Optical Sciences Centre, Swinburne University of Technology, Bioinformatics Division, Walter & Eliza Hall Institute of Medical Research – sequence: 10 givenname: Roberto orcidid: 0000-0001-7717-1519 surname: Morandotti fullname: Morandotti, Roberto organization: INRS-Énergie, Matériaux et Télécommunications, Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China – sequence: 11 givenname: Arnan orcidid: 0000-0002-2463-2956 surname: Mitchell fullname: Mitchell, Arnan organization: School of Engineering, RMIT University – sequence: 12 givenname: David J. orcidid: 0000-0001-5195-1744 surname: Moss fullname: Moss, David J. email: dmoss@swin.edu.au organization: Optical Sciences Centre, Swinburne University of Technology
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/33408378$$D View this record in MEDLINE/PubMed
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ContentType	Journal Article
Copyright	The Author(s), under exclusive licence to Springer Nature Limited 2020 Copyright Nature Publishing Group Jan 7, 2021
Copyright_xml	– notice: The Author(s), under exclusive licence to Springer Nature Limited 2020 – notice: Copyright Nature Publishing Group Jan 7, 2021
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Snippet	Convolutional neural networks, inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the...
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SubjectTerms	639/166/987 639/624/1111/1112 Accuracy Artificial neural networks Bands Bandwidths Cognitive tasks Complexity Computer vision Feature extraction Handwriting recognition Humanities and Social Sciences Learning algorithms Machine learning Medical imaging multidisciplinary Neural networks Object recognition Optical communication Pattern recognition Power Science Science (multidisciplinary) Speech recognition Temporal lobe Visual cortex Voice recognition
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Title	11 TOPS photonic convolutional accelerator for optical neural networks
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