Efficient FPGA mapping of Gilbert’s algorithm for SVM training on large-scale classification problems

Abstract

Support vector machines (SVMs) are an effective, adaptable and widely used method for supervised classification. However, training an SVM classifier on large-scale problems is proven to be a very time-consuming task for software implementations. This paper presents a scalable high-performance FPGA architecture of Gilbertpsilas Algorithm on SVM, which maximally utilizes the features of an FPGA device to accelerate the SVM training task for large-scale problems. Initial comparisons of the proposed architecture to the software approach of the algorithm show a speed-up factor range of three orders of magnitude for the SVM training time, regarding a wide range of datapsilas characteristics.