面向机器学习的代数整数量化

AI 摘要

该论文提出代数整数数值格式（AINF）及代数整数量化（AIQ），用于机器学习中的压缩计算。AINF 可归约为纯整数运算，实现无误差乘加，并支持按用例定制代数整数。作者开发了矩阵乘法引擎，性能在现代硬件加速器上处于高效数值实现的预期倍数内；在 LLM 推理的 8-bit 和 6-bit 量化实验中，AIQ 在相同每参数比特预算下精度与行业标准 MX 格式相当。

正文节选

Algebraic Integer Quantization for Machine Learning Applications Abstract In this article, we propose algebraic integer number formats (AINF) as a framework for highly configurable and accurate compressed computation. A key property of AINF is its straightforward reduction to purely integer arithmetic, enabling error-free multiply-accumulate operations. We develop a matrix multiplication engine that exploits this property, obtaining performance within a predictable factor of the most efficient n

发布时间：2026-10-12 12:00

抓取时间：2026-10-12 12:13

来源机构：arXiv