Silicon Photonics for Machine Learning: Training and Inference

被引：0

作者：

Shastri, B. J. ^{[1
,2
]}

Filipovich, M. J. ^{[1
]}

Guo, Z. ^{[1
]}

Prucnal, P. R. ^{[2
]}

Huang, C. ^{[2
]}

Tait, A. N. ^{[1
]}

Shekhar, S. ^{[3
]}

Sorger, V. J. ^{[4
]}

机构：

[1] Queens Univ, Dept Phys Engn Phys & Astron, Kingston, ON K7L 3N6, Canada

[2] Princeton Univ, Dept Elect Engn, Princeton, NJ 08544 USA

[3] Univ British Columbia, Dept Elect & Comp Engn, Vancouver, BC V6T 1Z4, Canada

[4] George Washington Univ, Dept Elect & Comp Engn, V6T 1Z4, Washington, DC USA

来源：

2022 EUROPEAN CONFERENCE ON OPTICAL COMMUNICATION (ECOC) | 2022年

关键词：

D O I：

暂无

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

Photonics neural networks employ optical device physics for neuron models, and optical interconnects for distributed, parallel, and analog processing for high-bandwidth, low-latency, and low-switching energy applications in AI and neuromorphic computing. We discuss silicon photonics for machine learning acceleration for inference and in situ training. (c) 2022 The Author(s)

引用

页数：3

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