Neuromorphic photonic networks using silicon photonic weight banks

Abstract Photonic systems for high-performance information processing have attracted renewed interest. Neuromorphic silicon photonics has the potential to integrate processing functions that vastly exceed the capabilities of electronics. We report first observations of a recurrent silicon photonic n...

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Autores principales:	Alexander N. Tait, Thomas Ferreira de Lima, Ellen Zhou, Allie X. Wu, Mitchell A. Nahmias, Bhavin J. Shastri, Paul R. Prucnal
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c4e16f7fb9404d928eeca66ff37ca512
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spelling	oai:doaj.org-article:c4e16f7fb9404d928eeca66ff37ca5122021-12-02T16:07:57ZNeuromorphic photonic networks using silicon photonic weight banks10.1038/s41598-017-07754-z2045-2322https://doaj.org/article/c4e16f7fb9404d928eeca66ff37ca5122017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-07754-zhttps://doaj.org/toc/2045-2322Abstract Photonic systems for high-performance information processing have attracted renewed interest. Neuromorphic silicon photonics has the potential to integrate processing functions that vastly exceed the capabilities of electronics. We report first observations of a recurrent silicon photonic neural network, in which connections are configured by microring weight banks. A mathematical isomorphism between the silicon photonic circuit and a continuous neural network model is demonstrated through dynamical bifurcation analysis. Exploiting this isomorphism, a simulated 24-node silicon photonic neural network is programmed using “neural compiler” to solve a differential system emulation task. A 294-fold acceleration against a conventional benchmark is predicted. We also propose and derive power consumption analysis for modulator-class neurons that, as opposed to laser-class neurons, are compatible with silicon photonic platforms. At increased scale, Neuromorphic silicon photonics could access new regimes of ultrafast information processing for radio, control, and scientific computing.Alexander N. TaitThomas Ferreira de LimaEllen ZhouAllie X. WuMitchell A. NahmiasBhavin J. ShastriPaul R. PrucnalNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alexander N. Tait Thomas Ferreira de Lima Ellen Zhou Allie X. Wu Mitchell A. Nahmias Bhavin J. Shastri Paul R. Prucnal Neuromorphic photonic networks using silicon photonic weight banks
description	Abstract Photonic systems for high-performance information processing have attracted renewed interest. Neuromorphic silicon photonics has the potential to integrate processing functions that vastly exceed the capabilities of electronics. We report first observations of a recurrent silicon photonic neural network, in which connections are configured by microring weight banks. A mathematical isomorphism between the silicon photonic circuit and a continuous neural network model is demonstrated through dynamical bifurcation analysis. Exploiting this isomorphism, a simulated 24-node silicon photonic neural network is programmed using “neural compiler” to solve a differential system emulation task. A 294-fold acceleration against a conventional benchmark is predicted. We also propose and derive power consumption analysis for modulator-class neurons that, as opposed to laser-class neurons, are compatible with silicon photonic platforms. At increased scale, Neuromorphic silicon photonics could access new regimes of ultrafast information processing for radio, control, and scientific computing.
format	article
author	Alexander N. Tait Thomas Ferreira de Lima Ellen Zhou Allie X. Wu Mitchell A. Nahmias Bhavin J. Shastri Paul R. Prucnal
author_facet	Alexander N. Tait Thomas Ferreira de Lima Ellen Zhou Allie X. Wu Mitchell A. Nahmias Bhavin J. Shastri Paul R. Prucnal
author_sort	Alexander N. Tait
title	Neuromorphic photonic networks using silicon photonic weight banks
title_short	Neuromorphic photonic networks using silicon photonic weight banks
title_full	Neuromorphic photonic networks using silicon photonic weight banks
title_fullStr	Neuromorphic photonic networks using silicon photonic weight banks
title_full_unstemmed	Neuromorphic photonic networks using silicon photonic weight banks
title_sort	neuromorphic photonic networks using silicon photonic weight banks
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/c4e16f7fb9404d928eeca66ff37ca512
work_keys_str_mv	AT alexanderntait neuromorphicphotonicnetworksusingsiliconphotonicweightbanks AT thomasferreiradelima neuromorphicphotonicnetworksusingsiliconphotonicweightbanks AT ellenzhou neuromorphicphotonicnetworksusingsiliconphotonicweightbanks AT alliexwu neuromorphicphotonicnetworksusingsiliconphotonicweightbanks AT mitchellanahmias neuromorphicphotonicnetworksusingsiliconphotonicweightbanks AT bhavinjshastri neuromorphicphotonicnetworksusingsiliconphotonicweightbanks AT paulrprucnal neuromorphicphotonicnetworksusingsiliconphotonicweightbanks
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Neuromorphic photonic networks using silicon photonic weight banks

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