Self-controlling photonic-on-chip networks with deep reinforcement learning

Abstract We present a novel photonic chip design for high bandwidth four-degree optical switches that support high-dimensional switching mechanisms with low insertion loss and low crosstalk in a low power consumption level and a short switching time. Such four-degree photonic chips can be used to bu...

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Autores principales:	Nguyen Do, Dung Truong, Duy Nguyen, Minh Hoai, Cuong Pham
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/17b98fa5209c4fa59d1328acecb91af3
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spelling	oai:doaj.org-article:17b98fa5209c4fa59d1328acecb91af32021-12-05T12:12:43ZSelf-controlling photonic-on-chip networks with deep reinforcement learning10.1038/s41598-021-02583-72045-2322https://doaj.org/article/17b98fa5209c4fa59d1328acecb91af32021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02583-7https://doaj.org/toc/2045-2322Abstract We present a novel photonic chip design for high bandwidth four-degree optical switches that support high-dimensional switching mechanisms with low insertion loss and low crosstalk in a low power consumption level and a short switching time. Such four-degree photonic chips can be used to build an integrated full-grid Photonic-on-Chip Network (PCN). With four distinct input/output directions, the proposed photonic chips are superior compared to the current bidirectional photonic switches, where a conventionally sizable PCN can only be constructed as a linear chain of bidirectional chips. Our four-directional photonic chips are more flexible and scalable for the design of modern optical switches, enabling the construction of multi-dimensional photonic chip networks that are widely applied for intra-chip communication networks and photonic data centers. More noticeably, our photonic networks can be self-controlling with our proposed Multi-Sample Discovery model, a deep reinforcement learning model based on Proximal Policy Optimization. On a PCN, we can optimize many criteria such as transmission loss, power consumption, and routing time, while preserving performance and scaling up the network with dynamic changes. Experiments on simulated data demonstrate the effectiveness and scalability of the proposed architectural design and optimization algorithm. Perceivable insights make the constructed architecture become the self-controlling photonic-on-chip networks.Nguyen DoDung TruongDuy NguyenMinh HoaiCuong PhamNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nguyen Do Dung Truong Duy Nguyen Minh Hoai Cuong Pham Self-controlling photonic-on-chip networks with deep reinforcement learning
description	Abstract We present a novel photonic chip design for high bandwidth four-degree optical switches that support high-dimensional switching mechanisms with low insertion loss and low crosstalk in a low power consumption level and a short switching time. Such four-degree photonic chips can be used to build an integrated full-grid Photonic-on-Chip Network (PCN). With four distinct input/output directions, the proposed photonic chips are superior compared to the current bidirectional photonic switches, where a conventionally sizable PCN can only be constructed as a linear chain of bidirectional chips. Our four-directional photonic chips are more flexible and scalable for the design of modern optical switches, enabling the construction of multi-dimensional photonic chip networks that are widely applied for intra-chip communication networks and photonic data centers. More noticeably, our photonic networks can be self-controlling with our proposed Multi-Sample Discovery model, a deep reinforcement learning model based on Proximal Policy Optimization. On a PCN, we can optimize many criteria such as transmission loss, power consumption, and routing time, while preserving performance and scaling up the network with dynamic changes. Experiments on simulated data demonstrate the effectiveness and scalability of the proposed architectural design and optimization algorithm. Perceivable insights make the constructed architecture become the self-controlling photonic-on-chip networks.
format	article
author	Nguyen Do Dung Truong Duy Nguyen Minh Hoai Cuong Pham
author_facet	Nguyen Do Dung Truong Duy Nguyen Minh Hoai Cuong Pham
author_sort	Nguyen Do
title	Self-controlling photonic-on-chip networks with deep reinforcement learning
title_short	Self-controlling photonic-on-chip networks with deep reinforcement learning
title_full	Self-controlling photonic-on-chip networks with deep reinforcement learning
title_fullStr	Self-controlling photonic-on-chip networks with deep reinforcement learning
title_full_unstemmed	Self-controlling photonic-on-chip networks with deep reinforcement learning
title_sort	self-controlling photonic-on-chip networks with deep reinforcement learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/17b98fa5209c4fa59d1328acecb91af3
work_keys_str_mv	AT nguyendo selfcontrollingphotoniconchipnetworkswithdeepreinforcementlearning AT dungtruong selfcontrollingphotoniconchipnetworkswithdeepreinforcementlearning AT duynguyen selfcontrollingphotoniconchipnetworkswithdeepreinforcementlearning AT minhhoai selfcontrollingphotoniconchipnetworkswithdeepreinforcementlearning AT cuongpham selfcontrollingphotoniconchipnetworkswithdeepreinforcementlearning
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Self-controlling photonic-on-chip networks with deep reinforcement learning

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