4 papers
Sparse Attention Remapping with Clustering for Efficient LLM Decoding on PIM
Zehao Fan, Garrett Gagnon, Zhenyu Liu +1
Transformer-based models are the foundation of modern machine learning, but their execution, particularly during autoregressive decoding in large language models (LLMs), places sig…
Bandwidth-Efficient Adaptive Mixture-of-Experts via Low-Rank Compensation
Zhenyu Liu, Yunzhen Liu, Zehao Fan +5
Mixture-of-Experts (MoE) models scale capacity via sparse activation but stress memory and bandwidth. Offloading alleviates GPU memory by fetching experts on demand, yet token-leve…
Context-Aware Mixture-of-Experts Inference on CXL-Enabled GPU-NDP Systems
Zehao Fan, Zhenyu Liu, Yunzhen Liu +4
Mixture-of-Experts (MoE) models scale large language models through conditional computation, but inference becomes memory-bound once expert weights exceed the capacity of GPU memor…
RULER-Bench: Probing Rule-based Reasoning Abilities of Next-level Video Generation Models for Vision Foundation Intelligence
Xuming He, Zehao Fan, Hengjia Li +7
Recent advances in video generation have enabled the synthesis of videos with strong temporal consistency and impressive visual quality, marking a crucial step toward vision founda…