18 citations · 29 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
PDNNet: PDN-Aware GNN-CNN Heterogeneous Network for Dynamic IR Drop Prediction
Yuxiang Zhao, Zhuomin Chai, Xun Jiang +3
IR drop on the power delivery network (PDN) is closely related to PDN's configuration and cell current consumption. As the integrated circuit (IC) design is growing larger, dynamic…
ProPD: Dynamic Token Tree Pruning and Generation for LLM Parallel Decoding
Shuzhang Zhong, Zebin Yang, Meng Li +3
Recent advancements in generative large language models (LLMs) have significantly boosted the performance in natural language processing tasks. However, their efficiency is hampere…