Comparative analysis of OpenCL vs. HDL with image-processing kernels on Stratix-V FPGA

Abstract

Application development with hardware description languages (HDLs) such as VHDL or Verilog involves numerous productivity challenges, limiting the potential impact of reconfigurable computing (RC) with FPGAs in high-performance computing. Major challenges with HDL design include steep learning curves, large and complex codes, long compilation times, and lack of development standards across platforms. A relative newcomer to RC, the Open Computing Language (OpenCL) reduces productivity hurdles by providing a platform-independent, C-based programming language. In this study, we conduct a performance and productivity comparison between three image-processing kernels (Canny edge detector, Sobel filter, and SURF feature-extractor) developed using Altera's SDK for OpenCL and traditional VHDL. Our results show that VHDL designs achieved a more efficient use of resources (59% to 70% less logic), however, both OpenCL and VHDL designs resulted in similar timing constraints (255MHz <; fmax <; 325MHz). Furthermore, we observed a 6× increase in productivity when using OpenCL development tools, as well as the ability to efficiently port the same OpenCL designs without change to three different RC platforms, with similar performance in terms of frequency and resource utilization.