基于Winograd算法的目标检测加速器设计与优化

李斌; 齐延荣; 周清雷

doi:10.12263/DZXB.20201371

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基于Winograd算法的目标检测加速器设计与优化

学术论文 | 更新时间：2025-12-08

- 基于Winograd算法的目标检测加速器设计与优化
- Design and Optimization of Target Detection Accelerator Based on Winograd Algorithm
- 电子学报 2022年50卷第10期页码：2387-2397
- 作者机构：
  
  1.郑州大学计算机与人工智能学院,河南郑州 450001
  2.郑州大学信息工程学院,河南郑州 450001
- 作者简介：
  
  [ "李斌男，1986年生，河南郑州人.主要研究方向为信息安全和高性能计算." ]
- 基金信息：
- DOI：10.12263/DZXB.20201371
  中图分类号： TP391.41;TP183
- 收稿：2020-12-01，
  
  修回：2021-08-17，
  
  纸质出版：2022-10-25
- 稿件说明：
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李斌,齐延荣,周清雷.基于Winograd算法的目标检测加速器设计与优化[J].电子学报,2022,50(10):2387-2397.

LI Bin,QI Yan-rong,ZHOU Qing-lei.Design and Optimization of Target Detection Accelerator Based on Winograd Algorithm[J].ACTA ELECTRONICA SINICA,2022,50(10):2387-2397.
李斌,齐延荣,周清雷.基于Winograd算法的目标检测加速器设计与优化[J].电子学报,2022,50(10):2387-2397. DOI： 10.12263/DZXB.20201371.

LI Bin,QI Yan-rong,ZHOU Qing-lei.Design and Optimization of Target Detection Accelerator Based on Winograd Algorithm[J].ACTA ELECTRONICA SINICA,2022,50(10):2387-2397. DOI： 10.12263/DZXB.20201371.

摘要

卷积神经网络（Convolutional Neural Networks，CNN）已被广泛应用于图像处理领域.基于CNN的目标检测模型，如YOLO，已被证明在许多应用中是最先进的.CNN对计算能力和内存带宽要求极高，通常需要部署到专用硬件平台，FPGA因其高性能、低功耗和可重配置性成为CNN的有效硬件加速器.以往的基于FPGA的目标检测加速器主要采用传统卷积算法，然而，传统卷积算法的高运算复杂度限制了加速器的性能.基于此，本文设计了一种基于Winograd算法的目标检测加速器.考虑到各模块间的联系，采用模块融合策略融合卷积层和池化层模块，降低数据移动次数，减少片外存储器访问次数，提高加速器整体性能.以YOLO2模型为例，对数据访问模式、池化内核、参数重排序、数据通路优化进行分析设计，并部署在U280板卡上.实验结果表明，量化后mAP降低了0.96%，性能达249.65 GOP/s，是Xilinx官网所给数据的4.4倍.

Abstract

Convolutional neural network(CNN) has been widely used in the field of image processing. CNN-based target detection models

such as YOLO

have proven to be the most advanced in many applications. CNN has extremely high requirements for computing power and memory bandwidth

and usually needs to be deployed on a dedicated hardware platform. FPGA has become an effective hardware accelerator for CNN due to its high performance

low power consumption and reconfigurability. In the past

FPGA-based target detection accelerators mainly used traditional convolution algorithms. However

the high computational complexity of traditional convolution algorithms limited the accelerator’s performance. Based on this

this paper designs a target detection accelerator based on Winograd algorithm. Taking into account the connection between the modules

the module fusion strategy is adopted to fuse the convolutional layer and the pooling layer modules to reduce the number of data movement

reduce the number of off-chip memory accesses

and improve the overall performance of the accelerator. Take the YOLO2 model as an example

analyze and design the data access mode

pooled kernel

parameter reordering

and data path optimization

and deploy it on the U280 board. The experimental results show that mAP is reduced by 0.96% after quantification

and the performance reaches 249.65GOP/s

which is 4.4 times the data given by Xilinx official website.

关键词

Keywords

references

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