| Computer vision algorithms are natural candidates for high performance computing due to their inherent parallelism and intense computational demands. For example, a simple 3 x 3 convolution on a 512 X 512 gray scale image at 30 frames per second requires 67.5 million multiplications and 60 million additions to be performed in one second. Computer vision tasks can be classified into three categories based on their computational complexity and communication complexity: low-level, intermediate-level and high-level. Special-purpose hardware provides better performance compared to a general-purpose hardware for all the three levels of vision tasks. With recent advances in very large scale integration (VLSI) technology, an application specific integrated circuit; (ASIC) can provide the best performance in terms of total execution time. However, long design cycle time, high development cost and inflexibility of a dedicated hardware deter design of ASICs. In contrast, field programmable gate arrays (FPGAs) support lower design verification time and easier design adaptability at a lower cost. Hence, FPGAs with an array of reconfigurable logic blocks can be very useful compute elements. FPGA-based custom computing machines are playing a major role in realizing high performance application accelerators. Three computer vision algorithms have been investigated for mapping onto custom computing machines: (i) template matching (convolution)--a low level vision operation; (ii) texture-based--segmentation an intermediate-level operation, and (iii) point pattern matching--a high level vision algorithm. The advantages demonstrated through these implementations are as follows. First, custom computing machines are suitable for all the three levels of computer vision algorithms. Second, custom computing machines can map all stages of a vision system easily. This is unlike typical hardware platforms where a separate subsystem is dedicated to a specific step of the vision algorithm. Third, custom computing approach can run a vision application at a high speed, often very close to the speed of special-purpose hardware. The performance of these algorithms on Splash 2--a Xilinx 4010 field programmable gate array-based custom computing machine--is near ASIC level of speed. A taxonomy involving custom computing platforms, special purpose vision systems, general purpose processors and special purpose ASICs has been constructed using several comparative features characterizing these systems and standard hierarchical clustering algorithms. The taxonomy provides an easy way of understanding the features of custom computing machines. |
