25 citations · 26 across the 3 of their papers we have counts for
3 papers
cs.LG2026
An Empirical Study of openPangu Quantization on Ascend NPUs
Tong Shi, Jiacheng Wang, Hui Xie +4
openPangu models are attractive targets for private and domestic large-language-model deployment, yet their robustness under aggressive post-training quantization on Ascend NPUs ha…
cs.LG2024★ 25 cited
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
Wei Huang, Yangdong Liu, Haotong Qin +5
Pretrained large language models (LLMs) exhibit exceptional general language processing capabilities but come with significant demands on memory and computational resources. As a p…
cs.LG2023★ 1 cited
On-Chip Hardware-Aware Quantization for Mixed Precision Neural Networks
Wei Huang, Haotong Qin, Yangdong Liu +4
Low-bit quantization emerges as one of the most promising compression approaches for deploying deep neural networks on edge devices. Mixed-precision quantization leverages a mixtur…