Journal
JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS
Volume 24, Issue 5, Pages -Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218126615500644
Keywords
Data mining; machine learning; decision trees; support vector machines; artificial neural networks; hardware implementation; reconfigurable hardware; FPGA; R project
Funding
- Serbian MNTR [TR32016]
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This paper proposes universal coarse-grained reconfigurable computing architecture for hardware implementation of decision trees (DTs), artificial neural networks (ANNs), and support vector machines (SVMs), suitable for both field programmable gate arrays (FPGA) and application specific integrated circuits (ASICs) implementation. Using this universal architecture, two versions of DTs (functional DT and axis-parallel DT), two versions of SVMs (with polynomial and radial kernel) and two versions of ANNs (multi layer perceptron ANN and radial basis ANN) machine learning classifiers, have been implemented in FPGA. Experimental results, based on 18 benchmark datasets of standard UCI machine learning repository database, show that FPGA implementation provides significant improvement (1-2 orders of magnitude) in the average instance classification time, in comparison with software implementations based on R project.
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