13 citations · 16 across the 15 of their papers we have counts for
9 papers · 1 filter
TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation
Junrui Xiao, Zhikai Li, Lianwei Yang +2
Post-training quantization (PTQ) reduces excessive hardware cost by quantizing full-precision models into lower bit representations on a tiny calibration set, without retraining. D…
Privacy-Preserving SAM Quantization for Efficient Edge Intelligence in Healthcare
Zhikai Li, Jing Zhang, Qingyi Gu
The disparity in healthcare personnel expertise and medical resources across different regions of the world is a pressing social issue. Artificial intelligence technology offers ne…
DilateQuant: Accurate and Efficient Diffusion Quantization via Weight Dilation
Xuewen Liu, Zhikai Li, Minhao Jiang +3
Model quantization is a promising method for accelerating and compressing diffusion models. Nevertheless, since post-training quantization (PTQ) fails catastrophically at low-bit c…
K-Sort Arena: Efficient and Reliable Benchmarking for Generative Models via K-wise Human Preferences
Zhikai Li, Xuewen Liu, Dongrong Joe Fu +4
The rapid advancement of visual generative models necessitates efficient and reliable evaluation methods. Arena platform, which gathers user votes on model comparisons, can rank mo…
DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers
Lianwei Yang, Haisong Gong, Haokun Lin +4
Vision Transformers (ViTs) have gained significant attention, but their high computing cost limits the practical applications. While post-training quantization (PTQ) reduces model…
MGRQ: Post-Training Quantization For Vision Transformer With Mixed Granularity Reconstruction
Lianwei Yang, Zhikai Li, Junrui Xiao +2
Post-training quantization (PTQ) efficiently compresses vision models, but unfortunately, it accompanies a certain degree of accuracy degradation. Reconstruction methods aim to enh…
RepQuant: Towards Accurate Post-Training Quantization of Large Transformer Models via Scale Reparameterization
Zhikai Li, Xuewen Liu, Jing Zhang +1
Large transformer models have demonstrated remarkable success. Post-training quantization (PTQ), which requires only a small dataset for calibration and avoids end-to-end retrainin…
LLM Inference Unveiled: Survey and Roofline Model Insights
Zhihang Yuan, Yuzhang Shang, Yang Zhou +11
The field of efficient Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Although the field has expanded and is v…
EDA-DM: Enhanced Distribution Alignment for Post-Training Quantization of Diffusion Models
Xuewen Liu, Zhikai Li, Junrui Xiao +3
Diffusion models have achieved great success in image generation tasks. However, the lengthy denoising process and complex neural networks hinder their low-latency applications in…