Photonic Max-Pooling for Deep Neural Networks Using a Programmable Photonic Platform

被引：4

作者：

Ashtiani, Farshid ^{[1
]}

On, Mehmet Berkay ^{[1
,2
]}

Sanchez-Jacome, David ^{[3
]}

Perez-Lopez, Daniel ^{[3
]}

Yoo, S. J. Ben ^{[2
]}

Blanco-Redondo, Andrea ^{[1
]}

机构：

[1] Nokia Bell Labs, 600 Mt Ave, Murray Hill, NJ 07974 USA

[2] Univ Calif Davis, Dept Elect & Comp Engn, 1 Shields Ave, Davis, CA 95616 USA

[3] iPron Programmable Photon, Avenida Blasco Ibanez 25, Valencia 46010, Spain

来源：

2023 OPTICAL FIBER COMMUNICATIONS CONFERENCE AND EXHIBITION, OFC | 2023年

关键词：

D O I：

10.1364/OFC.2023.M1J.6

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We propose a photonic max-pooling architecture for photonic neural networks which is compatible with integrated photonic platforms. As a proof of concept, we have experimentally demonstrated the max-pooling function on a programmable photonic platform consisting of a hexagonal mesh of Mach-Zehnder interferometers. (C) 2022 The Author(s)

引用

页数：3

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