English(EN) DyRA: Dynamic Residual Approximation for Efficient Matrix Multiplication in DNNs

DyRA方法通过纠正输出误差来提高DNN效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

研究人员开发了DyRA，一种提高深度神经网络（DNN）中矩阵乘法效率的新颖方法。DyRA在推理过程中动态地近似和纠正由结构化权重近似引入的输出误差，从而在相同的计算预算下获得更准确的结果。该方法在视觉、语音和语言模型中都显示出了一致的改进，提供了比仅依赖结构化权重近似的方法更好的准确性-效率权衡。 AI

影响 DyRA提供了一种更有效的方式来运行大型AI模型，有可能降低推理成本并提高各种AI应用的性能。

排序理由该集群包含一篇详细介绍提高DNN效率新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Daewon Chae, Hyunwon Chung, Changwoo Lee, Hun-Seok Kim · 2026-10-05 04:00

DyRA：DNN中用于高效矩阵乘法的动态残差近似

arXiv:2610.02882v1 Announce Type: cross Abstract: Large-scale foundation models achieve strong performance across diverse tasks, but their size makes inference costly, largely due to dense matrix multiplications. Prior work reduces this cost by replacing dense weight matrices wit…

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报道来源 [1]

DyRA：DNN中用于高效矩阵乘法的动态残差近似

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