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cs.LG2025
HybriMoE: Hybrid CPU-GPU Scheduling and Cache Management for Efficient MoE Inference
Shuzhang Zhong, Yanfan Sun, Ling Liang +3
The Mixture of Experts (MoE) architecture has demonstrated significant advantages as it enables to increase the model capacity without a proportional increase in computation. Howev…
cs.LG2024
MCUBERT: Memory-Efficient BERT Inference on Commodity Microcontrollers
Zebin Yang, Renze Chen, Taiqiang Wu +5
In this paper, we propose MCUBERT to enable language models like BERT on tiny microcontroller units (MCUs) through network and scheduling co-optimization. We observe the embedding…
cs.LG2024★ 18 cited
AdapMoE: Adaptive Sensitivity-based Expert Gating and Management for Efficient MoE Inference
Shuzhang Zhong, Ling Liang, Yuan Wang +3
Mixture-of-Experts (MoE) models are designed to enhance the efficiency of large language models (LLMs) without proportionally increasing the computational demands. However, their d…