新理论为量化矩阵乘法误差提供下界

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一个新的理论框架，以最小化量化矩阵乘法的误差。这项发表在arXiv上的研究引入了抖动标量量化的核范数下界，提供了一种优化保乘积变换的方法。该方法旨在通过调整因子范围和网格步长来减少量化误差，而不会改变最终的矩阵乘积。该论文表明，特定的构造，例如与Hadamard矩阵或离散余弦变换对齐的构造，可以达到或接近此下界，为高效矩阵运算提供了实际意义。 AI

影响通过优化的矩阵运算，为更高效的AI模型训练和推理提供了理论基础。

排序理由发表在arXiv上的学术论文，详细介绍了特定数学问题的新理论下界。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Piyush Sao, Narasinga Miniskar, Pedro Valero-Lara, Keita Teranishi, Sudip Seal · 2026-09-10 04:00

矩阵乘积的抖动标量量化的核范数下界

arXiv:2609.05641v1 Announce Type: cross Abstract: We consider the problem of minimizing error in quantized matrix multiplication $C=AB$. Scalar quantization of the factors introduces rounding errors whose scale depends on the maximum absolute entries -- the ranges -- of their row…

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矩阵乘积的抖动标量量化的核范数下界

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