3 papers
cs.LG2025
APB: Accelerating Distributed Long-Context Inference by Passing Compressed Context Blocks across GPUs
Yuxiang Huang, Mingye Li, Xu Han +7
While long-context inference is crucial for advancing large language model (LLM) applications, its prefill speed remains a significant bottleneck. Current approaches, including seq…
cs.DC2024
BurstAttention: An Efficient Distributed Attention Framework for Extremely Long Sequences
Ao Sun, Weilin Zhao, Xu Han +4
Effective attention modules have played a crucial role in the success of Transformer-based large language models (LLMs), but the quadratic time and memory complexities of these att…
cs.CV2023
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
Lingdong Kong, Yaru Niu, Shaoyuan Xie +39
Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse weather conditions, sensor failure, and noise contamination, is desirable for safety-critical a…