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ExpertCache: GPU-Efficient MoE Inference through Reinforcement Learning-Guided Expert Selection
TANG, Xunzhu; SUN, Tiezhu; SONG, Yewei et al.
2025 • The 41st International Conference on Software Maintenance and Evolution, New Ideas and Emerging Results Track
Peer reviewed
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https://hdl.handle.net/10993/65750
 

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Disciplines :
Computer science
Author, co-author :
TANG, Xunzhu  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
SUN, Tiezhu  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
SONG, Yewei  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
Siyuan Ma;  Nanyang Technological University
KLEIN, Jacques  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
BISSYANDE, Tegawendé François d Assise  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
External co-authors :
yes
Language :
English
Title :
ExpertCache: GPU-Efficient MoE Inference through Reinforcement Learning-Guided Expert Selection
Publication date :
07 September 2025
Event name :
The 41st International Conference on Software Maintenance and Evolution, New Ideas and Emerging Results Track
Event date :
7-12, September, 2025
By request :
Yes
Peer reviewed :
Peer reviewed
Source :
https://conf.researchr.org/details/icsme-2025/icsme-2025-nier/17/ExpertCache-GPU-Efficient-MoE-Inference-through-Reinforcement-Learning-Guided-Expert
Name of the research project :
R-AGR-3885 - H2020-ERC-NATURAL - BISSYANDE Tegawendé
Funding text :
This work is supported by the NATURAL project, which has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant No. 949014). The author Jiechao Gao is partially sponsored by funding from Yonghua Foundation.
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