Classifier Grouping to Enhance Data Locality for A Multi-Threaded Object Detection Algorithm

Abstract

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 multi-core 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.