Machine learning for photonics: from computing to communication

被引：0

作者：

Da Ros, Francesco ^{[1
]}

Cem, Ali ^{[1
]}

Osadchuk, Yevhenii ^{[1
]}

Jovanovic, Ognjen ^{[1
]}

Zibar, Darko ^{[1
]}

机构：

[1] Tech Univ Denmark, Lyngby, Denmark

来源：

2023 IEEE PHOTONICS SOCIETY SUMMER TOPICALS MEETING SERIES, SUM | 2023年

关键词：

NN models; matrix multipliers; equalization;

D O I：

10.1109/SUM57928.2023.10224400

中图分类号：

TB3 [工程材料学];

学科分类号：

0805 ; 080502 ;

摘要：

Neural networks are effective tools for learning direct and inverse models. Here, we review two specific applications of neural networks to photonics: (i) learning accurate direct models for optical matrix multipliers and (ii) inverse modeling for short-reach fiber communication systems, enabling signal equalization.

引用

页数：2

共 50 条

[1] Quantum Computing and Machine Learning on an Integrated Photonics Platform
Zhu, Huihui
Lin, Hexiang
Wu, Shaojun
Luo, Wei
Zhang, Hui
Zhan, Yuancheng
Wang, Xiaoting
Liu, Aiqun
Kwek, Leong Chuan
INFORMATION, 2024, 15 (02)
[2] Machine Learning With Neuromorphic Photonics
de Lima, Thomas Ferreira
Peng, Hsuan-Tung
Tait, Alexander N.
Nahmias, Mitchell A.
Miller, Heidi B.
Shastri, Bhavin J.
Prucnal, Paul R.
JOURNAL OF LIGHTWAVE TECHNOLOGY, 2019, 37 (05) : 1515 - 1534
[3] Photonics for computing and computing for photonics
Brunner, Daniel
Marandi, Alireza
Bogaerts, Wim
Ozcan, Aydogan
NANOPHOTONICS, 2020, 9 (13) : 4053 - 4054
[4] Machine learning and applications in ultrafast photonics
Goëry Genty
Lauri Salmela
John M. Dudley
Daniel Brunner
Alexey Kokhanovskiy
Sergei Kobtsev
Sergei K. Turitsyn
Nature Photonics, 2021, 15 : 91 - 101
[5] Machine learning and applications in ultrafast photonics
Genty, Goery
Salmela, Lauri
Dudley, John M.
Brunner, Daniel
Kokhanovskiy, Alexey
Kobtsev, Sergei
Turitsyn, Sergei K.
NATURE PHOTONICS, 2021, 15 (02) : 91 - 101
[6] Machine Learning for Integrated Quantum Photonics
Kudyshev, Zhaxylyk A.
Shalaev, Vladimir M.
Boltasseva, Alexandra
ACS PHOTONICS, 2021, 8 (01): : 34 - 46
[7] UbiNN: A Communication Efficient Framework for Distributed Machine Learning in Edge Computing
Li, Ke
Chen, Kexun
Luo, Shouxi
Zhang, Honghao
Fan, Pingzhi
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 2023, 10 (06): : 3368 - 3383
[8] Joint Communication and Computing Optimization for Hierarchical Machine Learning Tasks Distribution
Yang, Bo
Cao, Xuelin
Li, Xiangfang
Kroecker, Timothy
Qian, Lijun
2019 IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (ISCC), 2019, : 812 - 817
[9] SNAP: A Communication Efficient Distributed Machine Learning Framework for Edge Computing
Zhao, Yangming
Fan, Jingyuan
Su, Lu
Song, Tongyu
Wang, Sheng
Qiao, Chunming
2020 IEEE 40TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS), 2020, : 584 - 594
[10] Embedded Sensors, Communication Technologies, Computing Platforms and Machine Learning for UAVs: A Review
Wilson, A. N.
Kumar, Abhinav
Jha, Ajit
Cenkeramaddi, Linga Reddy
IEEE SENSORS JOURNAL, 2022, 22 (03) : 1807 - 1826

← 1 2 3 4 5 →