Efficient implementation of Sobel edge detection algorithm on CPU, GPU and FPGA

Abstract

Many applications in image processing have high degrees of inherent parallelism and are thus good candidates for parallel implementation. In fact, programming tools for field programmable gate array FPGA, SIMD instructions on CPU and a large number of cores on graphic processor unit GPU have been developed, but it is still difficult to achieve high performance on these platforms. This paper analyses the distinct features of compute unified device architecture CUDA GPU and summarises the general program mode of CUDA. Furthermore, we present three different implementations of Sobel edge detection on CPU, FPGA and GPU. Tested image data are also used in these hardware platforms to compare computational efficiency of CPU, GPU and FPGA.