3 papers
cs.LG2026
FAST: A Holistic Framework for Optimizing Memory-I/O, Computation, and Sampling in Temporal GNN Training
Yushu Cai, Qingrui Zhu, Lei Liu +3
Temporal Graph Neural Networks (TGNNs) are widely used for learning from dynamic graphs in applications such as recommendation, social network analysis, and traffic forecasting. Ho…
cs.LG2025
FlexQ: Efficient Post-training INT6 Quantization for LLM Serving via Algorithm-System Co-Design
Hao Zhang, Aining Jia, Weifeng Bu +4
Large Language Models (LLMs) demonstrate exceptional performance but entail significant memory and computational costs, restricting their practical deployment. While existing INT4/…
cs.LG2025
CARD: A Cache-Assisted Parallel Speculative Decoding Framework via Query-and-Correct Paradigm for Accelerating LLM Inference
Enyu Zhou, Kai Sheng, Hao Chen +1
Speculative decoding (SD), where a draft model provides multiple candidate tokens for the target model to verify in parallel, has demonstrated significant potential for acceleratin…