An FPGA Implementation of Linear Kernel Support Vector Machines

Abstract

This paper describes preliminary performance results of a reconfigurable hardware implementation of a support vector machine classifier, aimed at brain-computer interface applications, which require real-time decision making in a portable device. The main constraint of the design was that it could perform a classification decision within the time span of an evoked potential recording epoch of 300 ms, which was readily achieved for moderate-sized support vector sets. Regardless of its fixed-point implementation, the FPGA-based model achieves equivalent classification accuracies to those of its software-based, floating-point counterparts.