Fast-tunable Graphene-based AWGR for Deep Learning Training Networks-Reference-Cited by-同舟云学术

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Fast-tunable Graphene-based AWGR for Deep Learning Training Networks

Published:2024-08-04 Issue: Volume:1 Page:14-20
ISSN:
Container-title:Proceedings of the 1st SIGCOMM Workshop on Hot Topics in Optical Technologies and Applications in Networking
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Author:

Zhang Shicheng¹^ORCID,Xue Xuwei¹^ORCID,Guo Bingli¹^ORCID,Li Yixuan¹^ORCID,Li Wenzhe²^ORCID,Shen Shikui³^ORCID,Qian Haoze¹^ORCID,Yin Xiaojie⁴^ORCID,Wei Buzheng³^ORCID,Yuan Guojun²^ORCID,Tang Xiongyan³^ORCID,Huang Shanguo¹^ORCID

Affiliation:

1. State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, China

2. Institute of Computing Technology, Chinese Academy of Sciences, China

3. China Unicom Research Institute Beijing, China

4. Key Laboratory of Optoelectronic Materials and Devices, Chinese Academy of Sciences, China

Funder

State Key Laboratory of Advanced Optical Communication Systems and Networks

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3672201.3674121

Reference38 articles.

1. Drude‐Model Calculation of Dispersion Forces. I. General Theory

2. Sirius

3. Scalable reduction collectives with data partitioning-based multi-leader design

4. SALaR: Scalable and Adaptive Designs for Large Message Reduction Collectives

5. Blueconnect: Decomposing all-reduce for deep learning on heterogeneous network hierarchy;Cho Minsik;Proceedings of Machine Learning and Systems,2019

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