计算机工程2025，Vol.51Issue(9)：71-79,9.DOI:10.19678/j.issn.1000-3428.0069598
Winograd异构采样窗口卷积加速算子
Winograd Heterogeneous Sampling Window Convolution Acceleration Operator
摘要
Abstract
In recent years,Artificial Intelligence(AI)has been widely used in fields such as large models,autonomous driving,and robotics.As the core of AI,neural networks process big data,learn,adapt complex patterns,and perform various tasks.For the implementation of neural networks,convolution algorithms are often used to extract local features of the input data to help them learn to understand the structure and pattern of data such as images and sounds.However,convolution computation involves intensive multiplication and accumulation operations and is a time intensive process,thus becoming a major obstacle for the real-time implementation of neural networks.In this study,to accelerate the convolution algorithm at the hardware level,a Winograd convolution acceleration operator based on a heterogeneous sampling window,which adopts a heterogeneous 4×2 sampling window to improve data utilization,is proposed.Additionally,a Winograd hardware acceleration module is designed using a pipeline and fixed-point structure,and a ReLU module based on pooling fusion is proposed.A prototype verification experiment is conducted on a Field Programmable Gate Array(FPGA).Finally,the acceleration ratio of the single-channel original sliding window convolutions is 86.4 and that of the three-channel sliding window convolutions is 28.8.The amount of read and write data is reduced to 11.07％of the original,and the resource consumption is lower than that of the Winograd convolution acceleration operators of the same type.It has the ability to integrate and build convolutional neural networks on a large scale,and compared with the Fast Fourier Transformation(FFT),it has distinct advantages.关键词
Winograd/卷积加速算子/硬件加速/异构采样/现场可编辑逻辑门阵列Key words
Winograd/convolutional acceleration operator/hardware acceleration/heterogeneous sampling/Field Programmable Gate Array(FPGA)分类
信息技术与安全科学引用本文复制引用
彭允,王立军,王玉冰,梁磊,宋悦,邱橙,雷宇鑫,贾鹏,缪国庆,秦莉..Winograd异构采样窗口卷积加速算子[J].计算机工程,2025,51(9):71-79,9.基金项目
吉林省科技发展计划(20230201033GX) （20230201033GX）
长春市优秀青年科技人才(23YQ18) （23YQ18）
国家自然科学基金(62090054) （62090054）
国家重点研发计划(2022YFB2803500) （2022YFB2803500）
吉林省国际合作项目(20230502005GH) （20230502005GH）
中国工程院院地合作项目(JL2023-16). （JL2023-16）
