{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T14:03:08Z","timestamp":1769176988750,"version":"3.49.0"},"reference-count":33,"publisher":"World Scientific Pub Co Pte Lt","issue":"05","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2015,6]]},"abstract":"<jats:p> 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\u20132 orders of magnitude) in the average instance classification time, in comparison with software implementations based on R project. <\/jats:p>","DOI":"10.1142\/s0218126615500644","type":"journal-article","created":{"date-parts":[[2015,2,18]],"date-time":"2015-02-18T02:41:05Z","timestamp":1424227265000},"page":"1550064","source":"Crossref","is-referenced-by-count":17,"title":["Reconfigurable Hardware for Machine Learning Applications"],"prefix":"10.1142","volume":"24","author":[{"given":"Vuk S.","family":"Vranjkovi\u0107","sequence":"first","affiliation":[{"name":"Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovi\u0107a 6, Novi Sad 21000, Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rastislav J. 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