papers

Publications (34)

cs.CV2026

Sparse VideoGen2: Accelerate Video Generation with Sparse Attention via Semantic-Aware Permutation

Shuo Yang, Haocheng Xi, Yilong Zhao +10

Diffusion Transformers (DiTs) are essential for video generation but suffer from significant latency due to the quadratic complexity of attention. By computing only critical tokens…

cs.CL2025

LServe: Efficient Long-sequence LLM Serving with Unified Sparse Attention

Shang Yang, Junxian Guo, Haotian Tang +7

Large language models (LLMs) have shown remarkable potential in processing long sequences and complex reasoning tasks, yet efficiently serving these models remains challenging due…

cs.LG2021

NAAS: Neural Accelerator Architecture Search

Yujun Lin, Mengtian Yang, Song Han

Data-driven, automatic design space exploration of neural accelerator architecture is desirable for specialization and productivity. Previous frameworks focus on sizing the numeric…

cs.LG2026

FourTune: Towards Fully 4-Bit Efficient Post-Training for Diffusion Models

Bowen Xue, Zihan Min, Xingyang Li +8

Diffusion models have become a dominant paradigm for high-quality generative modeling, while post-training is essential for adapting them to diverse downstream applications. Howeve…

cs.CV2020

Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

Yujun Lin, Song Han, Huizi Mao +2

Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-…

cs.AR2025

LEGO: Spatial Accelerator Generation and Optimization for Tensor Applications

Yujun Lin, Zhekai Zhang, Song Han

Modern tensor applications, especially foundation models and generative AI applications require multiple input modalities (both vision and language), which increases the demand for…