Turkish Journal of Electrical Engineering and Computer Sciences




The support vector machine (SVM) is one of the highly powerful classifiers that have been shown to be capable of dealing with high-dimensional data. However, its complexity increases requirements of computational power. Recent technologies including the postgenome data of high-dimensional nature add further complexity to the construction of SVM classifiers. In order to overcome this problem, hardware implementations of the SVM classifier have been proposed to benefit from parallelism to accelerate the SVM. On the other hand, those implementations offer limited flexibility in terms of changing parameters and require the reconfiguration of the whole device. The latter interrupts the operation of other tasks placed on the hardware device. In this work, two flexible hardware implementations of the SVM classifier are proposed, namely A1 and A2 classifiers with successful applications in a microarray dataset. In addition, two dynamically and partially reconfigurable (DPR) architectures of the SVM classifier are presented. The A1 and A2 architectures have achieved up to 61$\times$ and 49$\times$ speed-up, respectively, over the equivalent general purpose processor. Furthermore, the DPR implementations achieved at least $\sim$8$\times$ reduction in reconfiguration time compared to non-DPR implementation. This is a significant achievement that can be easily adapted in other application domains of a similar nature.


Bioinformatics, data mining, field programmable gate arrays, supervised learning, support vector machine

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