BirdMoE: Reducing Communication Costs for Mixture-of-Experts Training Using Load-Aware Bi-random Quantization

Mixture-of-Experts (MoE) model parallelism is prevalent in training Large Language Models (e.g., ChatGPT). However, the intensive all-to-all collective communication of the MoE layer's intermediate computing results substantially degrades MoE training efficiency. In this paper, we propose BirdM...

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Vydané v:	2025 62nd ACM/IEEE Design Automation Conference (DAC) s. 1 - 7
Hlavní autori:	Wu, Donglei, Yang, Weihao, Zou, Xiangyu, Jia, Jinda, Tao, Dingwen, Xia, Wen, Tian, Zhihong
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 22.06.2025
Predmet:	Chatbots communication -Mixture-of-Experts training communication compression Computational modeling Costs Design automation Large language models load balance Load modeling Mixture-of-Experts training Parallel processing Quantization (signal) Training
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