FPGA-based system-on-chip architecture for real-time embedded vision systems

Abstract

A single-chip FPGA implementation of a vision core is an efficient way to design fast and compact embedded vision systems from the PCB design level. The scope of the research is to design a novel FPGA-based parallel architecture entirely with on-chip FPGA resources. We designed it by utilizing block RAMs and lO interfaces on the FPGA. As a result, the system is compact, fast and flexible. We tested this architecture for spatial domain filtering algorithms using a Xilinx Virtex-2 Pro (XC2VP30) FPGA. Our algorithm uses a vision core with a 100 MHz system clock which supports image processing on a low-resolution image of 128x128 pixels up to 200 images per second. The results are accurate and fast as the fastest FPGA implementations reported to date. The performance of the algorithms could be substantially improved by applying sufficient parallelism.