Performance Evaluation of Real-Time Video Processing Edge Detection on Various Platforms

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IEEE

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info:eu-repo/semantics/embargoedAccess

Özet

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.

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Anahtar Kelimeler

Video Processing, OpenCV, PYNQ-Z1 SoC, FPGA Vision, Overlay Design, Pipeline Architecture, Hardware Accelerator

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2023 IEEE 17th International Conference on Application of Information and Communication Technologies (AICT)

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TATAR, Güner, Salih BAYAR & İhsan ÇİÇEK. "Performance Evaluation of Real-Time Video Processing Edge Detection on Various Platforms". 2023 IEEE 17th International Conference on Application of Information and Communication Technologies (AICT), (2023).

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