Approximation- and Quantization-Aware Training for Graph Neural Networks-Reference-Cited by-同舟云学术

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Approximation- and Quantization-Aware Training for Graph Neural Networks

Published:2024-02 Issue:2 Volume:73 Page:599-612
ISSN:0018-9340
Container-title:IEEE Transactions on Computers
language:
Short-container-title:IEEE Trans. Comput.

Author:

Novkin Rodion¹^ORCID,Klemme Florian²^ORCID,Amrouch Hussam¹^ORCID

Affiliation:

1. Chair of AI Processor Design, TUM School of Computation, Information and Technology and Munich Institute of Robotics and Machine Intelligence, Technical University of Munich (TUM), Munich, Germany

2. Semiconductor Test and Reliability (STAR), University of Stuttgart, Stuttgart, Germany

Funder

Advantest

German Research Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Hardware and Architecture,Theoretical Computer Science,Software

Link

http://xplorestaging.ieee.org/ielx7/12/10400065/10335640.pdf?arnumber=10335640

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Survey of Computationally Efficient Graph Neural Networks for Reconfigurable Systems;Information;2024-06-28

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