计算机应用与软件2026，Vol.43Issue(5)：18-22,62,6.DOI:10.3969/j.issn.1000-386x.2026.05.003
基于TVM深度学习编译器的算子融合规则研究
RESEARCH ON OPERATOR FUSION RULES BASED ON TVM DEEP LEARNING COMPILER
赵薇 1李颖颖 2韩林3
作者信息
- 1. 郑州大学计算机与人工智能学院 河南 郑州 450001
- 2. 信息工程大学数学工程与先进计算国家重点实验室 河南 郑州 450001
- 3. 国家超算郑州中心 河南 郑州 450001
- 折叠
摘要
Abstract
TVM deep learning compiler provides three general operator fusion rules for different types of operators.In order to further increase the granularity of operator fusion in the TVMdeep learning compiler,two novel operator fusion rules are proposed,one for the computational structure that contains only the dimensionality reduction operation,and one for the computational structure that contains both the dimensionality reduction operation and the element-by-element operation.The experimental results show that after applying the novel operator fusion rules,the time of TVM reasoning about models such as GoogleNet,DenseNet,BVLC AlexNet,and Mobilenetv2 is reduced by 17.3%～32.2%.关键词
算子融合/TVM深度学习编译器/深度学习编译优化/模型推理Key words
Operator fusion/TVM deep learning compiler/Deep learning compilation optimization/Model inference分类
信息技术与安全科学引用本文复制引用
赵薇,李颖颖,韩林..基于TVM深度学习编译器的算子融合规则研究[J].计算机应用与软件,2026,43(5):18-22,62,6.
