Implementation of Accelerating Video Preprocessing based on ZYNQ Platform Resource Management. (May 2020)

• Record Type: Journal Article
• Authors: Sheng, Mengxue; Hou, Wanwan; Jiang, Juchao
• Is Part Of: Journal of physics. Volume 1544(2020)
• Publication Date: 2020-05
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1544/1/012112
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Embedded AI rised in recent years, AI algorithm deployment platforms need strong computing power, but embedded systems focus on the balance between performance and cost. The contradiction has become a great challenge for the development of embedded AI. This paper analyzes the "ARM+FPGA" architecture and NEON register resources of the ZYNQ series chips introduced by Xilinx, and proposes a method to reasonably schedule the above resources to accelerate video preprocessing when it is used as an edge processing platform for intelligent monitoring terminals. This method makes the video intelligent analysis process of the monitoring terminal achieve real-time.