Comparing FPGAs and GPUs for high-performance image processing applications
ABSTRACT Modern image enhancement techniques have been shown to be effective in improving the quality of imagery. However, the computational requirements of applying such algorithms to streams of video in real-time often cannot be satisfied by standard microprocessor-based systems. While a scaled solution involving clusters of microprocessors may provide the necessary arithmetic capacity, deployment is limited to data-center scenarios. What is needed is a way to perform these techniques in real time on embedded platforms. A new paradigm of computing utilizing special-purpose commodity hardware including Field-Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPU) has recently emerged as an alternative to parallel computing using clusters of traditional CPUs. Recent research has shown that for many applications, such as image processing techniques requiring intense computations and large memory spaces, these hardware platforms significantly outperform microprocessors. Furthermore, while microprocessor technology has begun to stagnate, GPUs and FPGAs have continued to improve exponentially. FPGAs, flexible and powerful, are best targeted at embedded, low-power systems and specific applications. GPUs, cheap and readily available, are available to most users through their standard desktop machines. Additionally, as fabrication scale continues to shrink, heat and power consumption issues typically limiting GPU deployment to high-end desktop workstations are becoming less of a factor. The ability to include these devices in embedded environments opens up entire new application domains. In this paper, we investigate two state-of-the-art image processing techniques, super-resolution and the average-bispectrum speckle method, and compare FPGA and GPU implementations in terms of performance, development effort, cost, deployment options, and platform flexibility.
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ABSTRACT: The use of FPGA circuits in imaging systems increases. They compete with other computing environments. The article describes the indications to be followed while choosing the type of image processing computing system taking under consideration the advantages and disadvantages of each technology: general purpose processor, digital signal processor, graphical processing unit, application specific Integrated circuit and field programmable gate array. Attention is drawn to various video transmission standards. The state of research and development trends in the field of FPGA-based image processing are briefly presented. A defining processing performance method for image processing is proposed. It is proven that for a pipeline architecture implemented in FPGA, a linear speedup is achieved and parallel efficiency is equal to one.Opto-Electronics Review 06/2012; 20(2). DOI:10.2478/s11772-012-0021-2 · 1.28 Impact Factor