🤖 AI Summary
Extremely low-bit (e.g., 4-bit) post-training quantization (PTQ) suffers severe accuracy degradation due to quantization noise. To address this, we propose the first model-level series expansion framework for PTQ—requiring neither calibration data nor fine-tuning—that losslessly decomposes a floating-point model into multiple low-bit basis models. Our key contributions include: (i) the first application of series expansion to neural network quantization; (ii) a multi-granularity (tensor-, layer-, and model-level) low-bit basis expansion with rigorous convergence guarantees; and (iii) the design of AbelianAdd/Mul operators that form an Abelian group, ensuring parallelizability and commutativity. Experiments demonstrate state-of-the-art performance: 4-bit ResNet-50 achieves 77.03% Top-1 accuracy—surpassing its full-precision baseline—and establishes new SOTA across diverse architectures and tasks in low-bit PTQ.
📝 Abstract
Post-Training Quantization (PTQ) converts pre-trained Full-Precision (FP) models into quantized versions without training. While existing methods reduce size and computational costs, they also significantly degrade performance and quantization efficiency at extremely low settings due to quantization noise. We introduce a deep model series expansion framework to address this issue, enabling rapid and accurate approximation of unquantized models without calibration sets or fine-tuning. This is the first use of series expansion for neural network quantization. Specifically, our method expands the FP model into multiple low-bit basis models. To ensure accurate quantization, we develop low-bit basis model expansions at different granularities (tensor, layer, model), and theoretically confirm their convergence to the dense model, thus restoring FP model accuracy. Additionally, we design AbelianAdd/Mul operations between isomorphic models in the low-bit expansion, forming an Abelian group to ensure operation parallelism and commutativity. The experiments show that our algorithm achieves state-of-the-art performance in low-bit settings; for example, 4-bit quantization of ResNet-50 surpasses the original accuracy, reaching 77.03%. The code will be made public.