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Evaluating RISC-V Vector Instruction Set Architecture Extension with Computer Vision Workloads

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Abstract

Computer vision (CV) algorithms have been extensively used for a myriad of applications nowadays. As the multimedia data are generally well-formatted and regular, it is beneficial to leverage the massive parallel processing power of the underlying platform to improve the performances of CV algorithms. Single Instruction Multiple Data (SIMD) instructions, capable of conducting the same operation on multiple data items in a single instruction, are extensively employed to improve the efficiency of CV algorithms. In this paper, we evaluate the power and effectiveness of RISC-V vector extension (RV-V) on typical CV algorithms, such as Gray Scale, Mean Filter, and Edge Detection. By our examinations, we show that compared with the baseline OpenCV implementation using scalar instructions, the equivalent implementations using the RV-V (version 0.8) can reduce the instruction count of the same CV algorithm up to 24x, when processing the same input images. Whereas, the actual performances improvement measured by the cycle counts is highly related with the specific implementation of the underlying RV-V co-processor. In our evaluation, by using the vector co-processor (with eight execution lanes) of Xuantie C906, vector-version CV algorithms averagely exhibit up to 2.98x performances speedups compared with their scalar counterparts.

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Authors and Affiliations

  1. National Engineering Research Center for Big Data Technology and System, Huazhong University of Science and Technology, Wuhan, 430074, China

    Ruo-Shi Li, Ping Peng, Zhi-Yuan Shao, Hai Jin & Ran Zheng

  2. Services Computing Technology and System Laboratory, Huazhong University of Science and Technology, Wuhan, 430074, China

    Ruo-Shi Li, Ping Peng, Zhi-Yuan Shao, Hai Jin & Ran Zheng

  3. Cluster and Grid Computing Lab, Huazhong University of Science and Technology, Wuhan, 430074, China

    Ruo-Shi Li, Ping Peng, Zhi-Yuan Shao, Hai Jin & Ran Zheng

Authors
  1. Ruo-Shi Li
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  2. Ping Peng
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  3. Zhi-Yuan Shao
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  4. Hai Jin
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Correspondence to Zhi-Yuan Shao.

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Li, RS., Peng, P., Shao, ZY. et al. Evaluating RISC-V Vector Instruction Set Architecture Extension with Computer Vision Workloads. J. Comput. Sci. Technol. 38, 807–820 (2023). https://doi.org/10.1007/s11390-023-1266-6

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  • DOI: https://doi.org/10.1007/s11390-023-1266-6

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