Comparison of GPU and FPGA Implementation of SVM Algorithm for Fast Image Segmentation

This paper presents preliminary implementation results of the SVM (Support Vector Machine) algorithm. SVM is a dedicated mathematical formula which allows us to extract selective objects from a picture and assign them to an appropriate class. Consequently, a black and white images reflecting an occurrence of the desired feature is derived from an original picture fed into the classifier. This work is primarily focused on the FPGA and GPU implementations aspects of the algorithm as well as on comparison of the hardware and software performance. A human skin classifier was used as an example and implemented both on Intel Xeon E5645.40 GHz, Xilinx Virtex-5 LX220 and Nvidia Tesla m2090. It is worth emphasizing that in case of FPGA implementation the critical hardware components were designed using HDL (Hardware Description Language), whereas the less demanding or standard ones such as communication interfaces, FIFO, FSMs were implemented in Impulse C. Such an approach allowed us both to cut a design time and preserve a high performance of the hardware classification module. In case of GPU implementation whole algorithm is implemented in CUDA.