Performance evaluation of real-time video processing edge detection on various platforms - BAYAR, SALİH (My University, 2023)
Abstract—As real-time video processing applications grow in complexity, they demand higher performance. Achieving such a performance must involve a delicate balance between design constraints and optimization of performance criteria. A vital aspect of this balance is the integration of application-specific accelerator designs to boost computational efficiency. To illustrate this, we applied Laplacian High-Pass filtering operations on real-time video signals across three hardware platforms an ARM processor, an ARM+FPGA-based SoC, and a single-core Intel i7 processor. We further analyzed these platforms’ priceperformance ratios. Our research revealed that the ARM+FPGAbased SoC executed the filtering algorithms 23.124 times faster than the ARM processor and 1.969 times faster than the Intel i7 processor. Additionally, the ARM+FPGA-based SoC also showed the highest price-performance efficiency. To offer readers a more visual understanding, we include a resource utilization graph for the SoC hardware accelerator development board, thus demonstrating the efficiency of each platform tested.
Keywords: Video processing; OpenCV; PYNQ-Z1 SoC; FPGA Vision; Overlay design; Pipeline architecture; Hardware accelerator