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cs.LG2025
BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models
Liulu He, Shenli Zheng, Karwei Sun +6
Rotations have become essential to state-of-the-art quantization pipelines for large language models (LLMs) by effectively smoothing outliers in weights and activations. However, f…
cs.LG2025
FBQuant: FeedBack Quantization for Large Language Models
Yijiang Liu, Hengyu Fang, Liulu He +4
Deploying Large Language Models (LLMs) on edge devices is increasingly important, as it eliminates reliance on network connections, reduces expensive API calls, and enhances user p…
cs.LG2024
SFC: Achieve Accurate Fast Convolution under Low-precision Arithmetic
Liulu He, Yufei Zhao, Rui Gao +2
Fast convolution algorithms, including Winograd and FFT, can efficiently accelerate convolution operations in deep models. However, these algorithms depend on high-precision arithm…