计算机工程2025，Vol.51Issue(6)：74-82,9.DOI:10.19678/j.issn.1000-3428.0069199
基于向量转换的卷积计算优化方法
Optimization Method for Convolutional Computing Based on Vector Transformation
王培吉 1邹承明2
作者信息
- 1. 武汉理工大学计算机与人工智能学院,湖北武汉 430000
- 2. 武汉理工大学计算机与人工智能学院,湖北武汉 430000||交通物联网技术湖北省重点实验室,湖北武汉 430000||鹏城实验室,广东 深圳 518055
- 折叠
摘要
Abstract
To solve efficiency problems in convolution calculations,this paper proposes a convolution calculation optimization method OAC.The objective is to improve the efficiency of convolution calculations to address the increasing demand for high convolution calculation speed in fields such as deep learning.The OAC method is based on vector conversion and involves a series of ingenious steps to optimize convolution calculations.First,the input matrix is concatenated row-by-row into a vector.Subsequently,the convolution kernel is stretched and transformed,and zeroes are padded at appropriate positions according to the width of the input matrix and size of the convolution kernel to form another vector.This transformation is designed to perform correct calculations with the transformed vectors of the input matrix and minimize redundant operations in the calculation process,thereby improving efficiency.Finally,other optimization methods are combined to accelerate the vector calculations.Experimental results show that the calculation speed of the OAC method is 58.9％and 90.1％higher than that of the traditional MEC method and the im2col method.Further,the memory usage is reduced by 53.7％compared with that of the MEC method.The OAC method has not only achieved significant results in computational efficiency,but also provided efficient and feasible solutions for computing tasks such as deep learning scheme.关键词
深度学习/卷积计算/卷积优化/向量转换/加速库Key words
deep learning/convolutional computation/convolutional optimization/vector transformation/acceleration library分类
计算机与自动化引用本文复制引用
王培吉,邹承明..基于向量转换的卷积计算优化方法[J].计算机工程,2025,51(6):74-82,9.
