Dynamic partial reconfiguration implementation of the SVM/KNN multi-classifier on FPGA for bioinformatics application.

Abstract

Bioinformatics data tend to be highly dimensional in nature thus impose significant computational demands. To resolve limitations of conventional computing methods, several alternative high performance computing solutions have been proposed by scientists such as Graphical Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs). The latter have shown to be efficient and high in performance. In recent years, FPGAs have been benefiting from dynamic partial reconfiguration (DPR) feature for adding flexibility to alter specific regions within the chip. This work proposes combing the use of FPGAs and DPR to build a dynamic multi-classifier architecture that can be used in processing bioinformatics data. In bioinformatics, applying different classification algorithms to the same dataset is desirable in order to obtain comparable, more reliable and consensus decision, but it can consume long time when performed on conventional PC. The DPR implementation of two common classifiers, namely support vector machines (SVMs) and K-nearest neighbor (KNN) are combined together to form a multi-classifier FPGA architecture which can utilize specific region of the FPGA to work as either SVM or KNN classifier. This multi-classifier DPR implementation achieved at least ~8x reduction in reconfiguration time over the single non-DPR classifier implementation, and occupied less space and hardware resources than having both classifiers. The proposed architecture can be extended to work as an ensemble classifier.