Optimized Realization of Sobel Edge Detection Algorithm for FT-M7002

doi:10.19678/j.issn.1000-3428.0061903

Abstract

Abstract: Edge detection is a robust image analysis method used in image processing and computer vision.The Sobel operator is widely used in edge detection and image processing.With the development of domestic FT series high-performance Digital Signal Processors (DSP), the demand for FT platforms in image processing is increasing.Moreover, it is urgent to implement high-performance image-processing algorithms for FT platforms.The vector parallel optimization of the Sobel edge detection algorithm was performed on the FT-M7002 platform to solve the above problem.Single Instruction Multiple Data (SIMD) instructions embedded in the FT-M7002 processor were used to mine the data-level parallelism in the Sobel edge detection algorithm.In addition, a parallel conversion interface between the character and integer data was designed and implemented.The loop unrolling optimization method was used to improve the number of instruction beats, and the problem of discontinuous data access and memory was solved using Direct Memory Access (DMA) matrix transposition.Double buffer technology was used to achieve parallel data transmission and kernel computing to eliminate the time gap between data transmission and computing.The performance of the original Sobel algorithm and the optimization algorithm under various convolution kernel sizes and picture sizes were compared and analyzed.The results showed that compared with the original algorithm, the optimization algorithm could achieve an acceleration ratio of 1.66~3.14 times.Compared with the operation results obtained using the TMS320C6678 processor, the optimization algorithm could achieve an acceleration effect of 1.87~2.08 times on the FT-M7002 platform.

Key words: edge detection, Sobel operator, high-performance Digital Signal Processor(DSP), vector parallel, loop unrolling

摘要： 边缘检测是图像处理与计算机视觉领域中一种重要的图像分析方法，Sobel算子常用于粗精度的边缘提取，在图像边缘检测中被广泛应用。随着国产飞腾（FT）系列高性能数字信号处理器的发展，图像处理领域对FT平台的需求日益提高，同时急需实现面向FT平台的高性能图像处理算法。针对上述问题，在FT-M7002平台上对Sobel边缘检测算法进行向量并行优化，使用FT-M7002处理器内嵌SIMD指令，挖掘Sobel边缘检测算法中的数据级并行性，同时设计并实现一种字符型与整型数据间的并行化转换接口，使用循环展开优化方法提升指令节拍数，通过DMA矩阵转置解决数据访存不连续的问题。采用双缓冲技术实现数据传输与内核计算的并行，从而隐藏数据传输与计算之间的时间间隙。对比分析多种卷积核大小及图片规模下原Sobel算法与优化算法的性能，结果表明，与原始算法相比，该优化算法能取得1.66~3.14倍的加速比，此外，相较TMS320C6678处理器上的运行结果，在FT-M7002平台上优化算法可达到1.87~2.08倍的加速效果。

关键词: 边缘检测, Sobel算子, 高性能数字信号处理器, 向量并行, 循环展开

CLC Number:

TP391

FAN Mingliang, GUO Zihan, CHAI Xiaonan, SHANG Jiandong. Optimized Realization of Sobel Edge Detection Algorithm for FT-M7002[J]. Computer Engineering, 2022, 48(6): 193-199.

范明亮, 郭子涵, 柴晓楠, 商建东. 面向FT-M7002的Sobel边缘检测算法优化实现[J]. 计算机工程, 2022, 48(6): 193-199.

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References

[1] 周佩, 周维超, 王凯凯.TMS320C6678多核DSP并行访问存储器性能的研究[J].微型机与应用, 2014, 33(13):20-24. ZHOU P, ZHOU W C, WANG K K.Research on the performance of TMS320C6678 multicore DSP parallel memory access[J].Microcomputer & Its Applications, 2014, 33(13):20-24.(in Chinese)
[2] 赵一霈.DSP的发展与应用[J].电子技术与软件工程, 2018(6):92. ZHAO Y P.Development and application of DSP[J].Electronic Technology & Software Engineering, 2018(6):92.(in Chinese)
[3] 张军阳, 郭阳, 扈啸.二维矩阵卷积的并行计算方法[J].浙江大学学报(工学版), 2018, 52(3):515-523. ZHANG J Y, GUO Y, HU X.Parallel computing method for two-dimensional matrix convolution[J].Journal of Zhejiang University(Engineering Science), 2018, 52(3):515-523.(in Chinese)
[4] 李勇, 陈书明, 陈胜刚.一种基于YHFT-Matrix DSP的去块效应滤波算法的向量化实现[J].计算机工程与科学, 2014, 36(2):206-210. LI Y, CHEN S M, CHEN S G.Vectorizable implementation of adaptive deblocking filter on YHFT-Matrix DSP[J].Computer Engineering and Science, 2014, 36(2):206-210.(in Chinese)
[5] LIU F, GUAN G X, ZHAO H M, et al.Fast processing of UAV remote sensing image based on DSP hardware simulation platform[J].Journal of Physics Conference Series, 2020, 1486(7):072072.
[6] OKAMOTO T, KOIDE T, YOSHIDA S, et al.Implementation of computer-aided diagnosis system on customizable DSP core for colorectal endoscopic images with CNN features and SVM[C]//Proceedings of 2018 IEEE Region Conference.Washington D.C., USA:IEEE Press, 2018:1663-1666.
[7] AKASAPU A, SAILAJA V, PRASAD G.Implementation of Sobel filter using CUDA[J].IOP Conference Series:Materials Science and Engineering, 2021, 1045(1):012016.
[8] 罗紫阳, 李雪梅, 陈鹏.基于FPGA的Sobel算子边缘检测算法的实现[J].电子制作, 2020(21):6-7. LUO Z Y, LI X M, CHEN P.Implementation of Sobel operator edge detection algorithm based on FPGA[J].Electronic Manufacturing, 2020(21):6-7.(in Chinese)
[9] YANG S H, HSIAO S J.H.266/VVC fast intra prediction using Sobel edge features[J].Electronics Letters, 2021, 57(1):11-13.
[10] 陈鹏.图像Sobel处理的FPGA多核矩阵实现[D].沈阳:沈阳理工大学, 2013. CHEN P.FPGA multicore matrix's achieve of the image Sobel processing[D].Shenyang:Shenyang Ligong University, 2013.(in Chinese)
[11] 左颢睿, 张启衡, 徐勇, 等.基于GPU的快速Sobel边缘检测算法[J].光电工程, 2009, 36(1):8-12. ZUO H R, ZHANG Q H, XU Y, et al.Fast sobel edge detection algorithm based on GPU[J].Opto-Electronic Engineering, 2009, 36(1):8-12.(in Chinese)
[12] 王威.基于ARM-FPGA异构多核平台上图像处理算法的加速研究[D].西安:西安电子科技大学, 2019. WANG W.Accelerated research on image processing algorithm based on ARM-FPGA multi-core platform[D].Xi'an:Xidian University, 2019.(in Chinese)
[13] HADI SAPUTRA V, HERWINDIATI D E, SUTRISNO T.Car shape clustering using Sobel edge detection with divisive average linkage and single linkage algorithm (case:bus, Sedan, citycar, mpv, and truck)[J].IOP Conference Series:Materials Science and Engineering, 2020, 1007(1):012136.
[14] TIAN R, SUN G L, LIU X C, et al.Sobel edge detection based on weighted nuclear norm minimization image denoising[J].Electronics, 2021, 10(6):655.
[15] 李丹阳.基于Soble算子的图像边缘检测优化设计[J].数字技术与应用, 2017(11):137-138. LI D Y.A optimization design of edge detection based on Soble algorithm[J].Digital Technology and Application, 2017(11):137-138.(in Chinese)
[16] 缪成根, 刘琛.基于改进索贝尔算子的灰度图像边缘检测[J].物联网技术, 2020, 10(11):37-38, 41. MIAO C G, LIU C.Gray image edge detection based on improved Sobel operator[J].Internet of Things Technologies, 2020, 10(11):37-38, 41.(in Chinese)
[17] RAVIVARMA G, GAVASKAR K, MALATHI D, et al.Implementation of Sobel operator based image edge detection on FPGA[J].Materials Today:Proceedings, 2021, 45:2401-2407.
[18] WANTO A, RIZKI S D, ANDINI S, et al.Combination of Sobel+Prewitt edge detection method with Roberts+Canny on passion flower image identification[J].Journal of Physics:Conference Series, 2021, 1933(1):012037.
[19] 孙广辉, 扈啸, 王蕊.基于FT-M7002的OpenCV移植与优化[C]//第二十二届计算机工程与工艺年会暨第八届微处理器技术论坛论文集.[出版地不详]:中国计算机学会计算机工程与工艺专业委员会, 2018:9. SUN G H, HU X, WANG R.Transplantation and optimization of OpenCV based on FT-M7002[C]//Proceedings of the 22nd Annual Conference on Computer Engineering and Technology and the 8th Microprocessor Technology Forum.[S.l.]:Professional Committee of Computer Engineering and Technology of China Computer Society, 2018:9(in Chinese)
[20] 沈洁, 龙标, 姜浩, 等.飞腾处理器上向量三角函数的设计实现与优化[J].计算机研究与发展, 2020, 57(12):2610-2620. SHEN J, LONG B, JIANG H, et al.Implementation and optimization of vector trigonometric functions on Phytium processors[J].Journal of Computer Research and Development, 2020, 57(12):2610-2620.(in Chinese)
[21] 郭恒亮, 柴晓楠, 韩林, 等.Canny边缘检测算法在飞腾平台上的实现与优化[J].计算机工程, 2021, 47(7):37-43. GUO H L, CHAI X N, HAN L, et al.Implementation and optimization of Canny edge detection algorithm on FT platform[J].Computer Engineering, 2021, 47(7):37-43.(in Chinese)
[22] 孙广辉.基于FT-M7002的OpenCV移植与优化[D].西安:西安电子科技大学, 2019. SUN G H.OpenCV transplantation and optimization based on FT-M7002[D].Xi'an:Xidian University, 2019.(in Chinese)

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