标题： FP=xINT：一种用于训练后量化的小比特级数展开算法 显示英文标题

标题： FP=xINT:A Low-Bit Series Expansion Algorithm for Post-Training Quantization

摘要： 后训练量化（PTQ）将预训练的全精度（FP）模型转换为量化版本，而无需训练。 虽然现有方法减少了大小和计算成本，但由于量化噪声，在极低设置下它们也会显著降低性能和量化效率。 我们引入了一个深度模型系列扩展框架来解决这个问题，使得在没有校准集或微调的情况下能够快速且准确地近似未量化的模型。 这是首次将系列扩展用于神经网络量化。 具体来说，我们的方法将FP模型扩展为多个低比特基础模型。 为了确保准确量化，我们在不同粒度（张量、层、模型）上开发了低比特基础模型扩展，并理论上确认它们收敛到密集模型，从而恢复FP模型的准确性。 此外，我们在低比特扩展中的同构模型之间设计了AbelianAdd/Mul操作，形成一个Abelian群以确保操作的并行性和交换性。 实验表明，我们的算法在低比特设置中实现了最先进的性能；例如，ResNet-50的4比特量化超过了原始准确性，达到77.03%。 代码将公开。 显示英文摘要

摘要： 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.