FPGA Implementation of a Support Vector Machine Based Classification System and Its Potential Application in Smart Grid

Support Vector Machines (SVMs) is a popular classification and regression prediction tool that uses supervised machine learning theory to maximize the predictive accuracy. This paper focuses on the field programmable gate array (FPGA) implementation of a Support Vector Machine classification system. Owing to the advanced parallel calculation feature provided by FPGA, a fast data classification can be achieved by the FPGA-based two-class SVM classifier. The classification system works both in linear mode and non-linear mode, depending on the dimensions of the classification. Simulated results demonstrate that the classification system is effective in fast data classification, and it is a promising technique for Smart Grid to strengthen its communication security.