Classifier grouping to enhance data locality for a multi-threaded object detection algorithm

Bo-Cheng Lai*, Chih Hsuan Chiang, Guan Ru Li

*此作品的通信作者

    研究成果: Conference contribution同行評審

    4 引文 斯高帕斯(Scopus)

    摘要

    Object detection has become an enabling function for modern smart embedded devices to perform intelligent applications and interact with the environment appropriately and promptly. However, the limited computation resource of embedded devices has become a barrier to execute the computation intensive object detection algorithm. Leveraging the multi-threading scheme on embedded multi-core systems provides an opportunity to boost the performance. However, the memory bottleneck limits the performance scalability. Improving data locality of applications and maximizing the data reuse for on-chip caches have therefore become critical design concerns. This paper comprehensively analyzes the memory behavior and data locality of a multi-threaded object detection algorithm. A novel Classifier-Grouping scheme is proposed to significantly enhance the data reuse for on-chip caches of embedded multicore systems. By executing a multi-threaded object detection algorithm on a cycle-accurate multi-core simulator, the proposed approach can achieve up to 62% better performance when compared with the original parallel program.

    原文English
    主出版物標題Proceedings - 2011 17th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2011
    頁面268-275
    頁數8
    DOIs
    出版狀態Published - 1 12月 2011
    事件2011 17th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2011 - Tainan, Taiwan
    持續時間: 7 12月 20119 12月 2011

    出版系列

    名字Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS
    ISSN(列印)1521-9097

    Conference

    Conference2011 17th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2011
    國家/地區Taiwan
    城市Tainan
    期間7/12/119/12/11

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