8 papers
UECP: Uncertainty-Enhanced Collaborative Perception
Kang Yang, Tianci Bu, Peng Wang +3
Collaborative perception serves as a pivotal solution to enhance the perception capability of individual agents in autonomous driving, where a core challenge lies in seeking reliab…
Tackling the Data-Parallel Load Balancing Bottleneck in LLM Serving: Practical Online Routing at Scale
Tianci Bu, Yuan Lyu, Zixi Chen +6
Data-parallel (DP) load balancing has emerged as a first-order bottleneck in large-scale LLM serving. When a model is sharded across devices via tensor parallelism (TP) or expert p…
BOLT: Online Lightweight Adaptation for Preparation-Free Heterogeneous Cooperative Perception
Kang Yang, Tianci Bu, Peng Wang +2
Most existing heterogeneous cooperative perception methods depend on prior preparation like offline joint training or tailored collaborator-model adaptation. Such preprocessing is,…
EIMC: Efficient Instance-aware Multi-modal Collaborative Perception
Kang Yang, Peng Wang, Lantao Li +4
Multi-modal collaborative perception calls for great attention to enhancing the safety of autonomous driving. However, current multi-modal approaches remain a ``local fusion to com…
GGBall: Graph Generative Model on Poincaré Ball
Tianci Bu, Chuanrui Wang, Hao Ma +3
Generating graphs with hierarchical structures remains a fundamental challenge due to the limitations of Euclidean geometry in capturing exponential complexity. Here we introduce \…
A Universal Load Balancing Principle and Its Application to Large Language Model Serving
Zixi Chen, Tianci Bu, Chendong Song +3
Over 40% of computational power in Large Language Model (LLM) serving systems can be systematically wasted - not from hardware limits, but from load imbalance in barrier-synchroniz…