Hybrid photonic integrated circuits for neuromorphic computing [Invited]

The burgeoning of artificial intelligence has brought great convenience to people’s lives as large-scale computational models have emerged. Artificial intelligence-related applications, such as autonomous driving, medical diagnosis, and speech recognition, have experienced remarkable progress in rec...

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Hlavní autoři: Xu, R, Taheriniya, S, Ovvyan, AP, Bankwitz, JR, McRae, L, Jung, E, Brückerhoff-Plückelmann, F, Bente, I, Lenzini, F, Bhaskaran, H, Pernice, WHP
Médium: Journal article
Jazyk:English
Vydáno: Optica Publishing Group 2023
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author Xu, R
Taheriniya, S
Ovvyan, AP
Bankwitz, JR
McRae, L
Jung, E
Brückerhoff-Plückelmann, F
Bente, I
Lenzini, F
Bhaskaran, H
Pernice, WHP
author_facet Xu, R
Taheriniya, S
Ovvyan, AP
Bankwitz, JR
McRae, L
Jung, E
Brückerhoff-Plückelmann, F
Bente, I
Lenzini, F
Bhaskaran, H
Pernice, WHP
author_sort Xu, R
collection OXFORD
description The burgeoning of artificial intelligence has brought great convenience to people’s lives as large-scale computational models have emerged. Artificial intelligence-related applications, such as autonomous driving, medical diagnosis, and speech recognition, have experienced remarkable progress in recent years; however, such systems require vast amounts of data for accurate inference and reliable performance, presenting challenges in both speed and power consumption. Neuromorphic computing based on photonic integrated circuits (PICs) is currently a subject of interest to achieve high-speed, energy-efficient, and low-latency data processing to alleviate some of these challenges. Herein, we present an overview of the current photonic platforms available, the materials which have the potential to be integrated with PICs to achieve further performance, and recent progress in hybrid devices for neuromorphic computing.
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spelling oxford-uuid:316e0aee-2cec-40f4-84d7-b45f68b31cc32024-03-15T15:25:52ZHybrid photonic integrated circuits for neuromorphic computing [Invited]Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:316e0aee-2cec-40f4-84d7-b45f68b31cc3EnglishSymplectic ElementsOptica Publishing Group2023Xu, RTaheriniya, SOvvyan, APBankwitz, JRMcRae, LJung, EBrückerhoff-Plückelmann, FBente, ILenzini, FBhaskaran, HPernice, WHPThe burgeoning of artificial intelligence has brought great convenience to people’s lives as large-scale computational models have emerged. Artificial intelligence-related applications, such as autonomous driving, medical diagnosis, and speech recognition, have experienced remarkable progress in recent years; however, such systems require vast amounts of data for accurate inference and reliable performance, presenting challenges in both speed and power consumption. Neuromorphic computing based on photonic integrated circuits (PICs) is currently a subject of interest to achieve high-speed, energy-efficient, and low-latency data processing to alleviate some of these challenges. Herein, we present an overview of the current photonic platforms available, the materials which have the potential to be integrated with PICs to achieve further performance, and recent progress in hybrid devices for neuromorphic computing.
spellingShingle Xu, R
Taheriniya, S
Ovvyan, AP
Bankwitz, JR
McRae, L
Jung, E
Brückerhoff-Plückelmann, F
Bente, I
Lenzini, F
Bhaskaran, H
Pernice, WHP
Hybrid photonic integrated circuits for neuromorphic computing [Invited]
title Hybrid photonic integrated circuits for neuromorphic computing [Invited]
title_full Hybrid photonic integrated circuits for neuromorphic computing [Invited]
title_fullStr Hybrid photonic integrated circuits for neuromorphic computing [Invited]
title_full_unstemmed Hybrid photonic integrated circuits for neuromorphic computing [Invited]
title_short Hybrid photonic integrated circuits for neuromorphic computing [Invited]
title_sort hybrid photonic integrated circuits for neuromorphic computing invited
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