4 citations · 7 across the 5 of their papers we have counts for
5 papers
Error Propagation Mechanisms and Compensation Strategies for Quantized Diffusion
Songwei Liu, Chao Zeng, Chenqian Yan +4
Diffusion models have transformed image synthesis by establishing unprecedented quality and creativity benchmarks. Nevertheless, their large-scale deployment faces challenges due t…
GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference
Chao Zeng, Songwei Liu, Shu Yang +3
Model compression has emerged as a mainstream solution to reduce memory usage and computational overhead. This paper presents Group Quantization and Sparse Acceleration (GQSA), a n…
ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models
Chao Zeng, Songwei Liu, Yusheng Xie +6
Large Language Models (LLMs) have revolutionized natural language processing tasks. However, their practical application is constrained by substantial memory and computational dema…
Hybrid SD: Edge-Cloud Collaborative Inference for Stable Diffusion Models
Chenqian Yan, Songwei Liu, Hongjian Liu +5
Stable Diffusion Models (SDMs) have shown remarkable proficiency in image synthesis. However, their broad application is impeded by their large model sizes and intensive computatio…
FoldGPT: Simple and Effective Large Language Model Compression Scheme
Songwei Liu, Chao Zeng, Lianqiang Li +4
The demand for deploying large language models(LLMs) on mobile devices continues to increase, driven by escalating data security concerns and cloud costs. However, network bandwidt…