Video Analytic for Human Management and Security and FPGA Accelerated High Concurrency
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Abstract
This paper explores the use of video analytics by leveraging accelerated FPGA technology in combination with high-performance computing, specifically utilizing two Xilinx Alveo U50Lv cards and one U55C card. While many applications exist for motion analysis and detection in videos, the use of FPGAs in this context remains relatively scarce. FPGAs offer significant advantages in terms of energy efficiency and throughput. We present results demonstrating the parallelism capabilities in terms of the number of threads within a single Docker container that shares stack memory, as well as across multiple Docker containers. When operating within a single Docker process, the application shares the same memory space and resources, making it ideal for tasks that require efficient communication or data sharing. In contrast, running in multiple containers isolates processes, each with its own environment, and can significantly increase the number of threads. Our findings show that the combination of these techniques offers optimal performance for video analytics.
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