Machine learning on FPGA CAD
Parameter tuning for field-programmable gate array (FPGA) computer-aided design (CAD) tools was a difficult task. Plunify proposed a novel solution by incorporating machine learning. A classifier was built and refined iteratively from CAD run records; and it was used to predict whether a new set of...
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sg-ntu-dr.10356-657382023-03-03T20:35:17Z Machine learning on FPGA CAD Que, Yanghua Nachiket Kapre School of Computer Engineering DRNTU::Engineering::Electrical and electronic engineering Parameter tuning for field-programmable gate array (FPGA) computer-aided design (CAD) tools was a difficult task. Plunify proposed a novel solution by incorporating machine learning. A classifier was built and refined iteratively from CAD run records; and it was used to predict whether a new set of parameters would produce a good design. The machine learning routine has been proven effective; and this paper is an extension aimed to improve performance of the method. We experimented with feature selection and discovered only about 10-20 parameters in the group of 60 were relevant. We also constructed ensemble classifiers that helped to drive Area Under Curve (AUC) score from 0.75 to 0.79; and we further pushed the number to 0.82 when combining ensemble learning and feature selection. Bachelor of Engineering (Computer Science) 2015-12-10T09:01:55Z 2015-12-10T09:01:55Z 2015 2015 Final Year Project (FYP) http://hdl.handle.net/10356/65738 en Nanyang Technological University application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering Que, Yanghua Machine learning on FPGA CAD |
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Parameter tuning for field-programmable gate array (FPGA) computer-aided design (CAD) tools was a difficult task. Plunify proposed a novel solution by incorporating machine learning. A classifier was built and refined iteratively from CAD run records; and it was used to predict whether a new set of parameters would produce a good design. The machine learning routine has been proven effective; and this paper is an extension aimed to improve performance of the method. We experimented with feature selection and discovered only about 10-20 parameters in the group of 60 were relevant. We also constructed ensemble classifiers that helped to drive Area Under Curve (AUC) score from 0.75 to 0.79; and we further pushed the number to 0.82 when combining ensemble learning and feature selection. |
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Nachiket Kapre |
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Nachiket Kapre Que, Yanghua |
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Final Year Project |
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Que, Yanghua |
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Que, Yanghua |
title |
Machine learning on FPGA CAD |
title_short |
Machine learning on FPGA CAD |
title_full |
Machine learning on FPGA CAD |
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Machine learning on FPGA CAD |
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Machine learning on FPGA CAD |
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machine learning on fpga cad |
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2015 |
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http://hdl.handle.net/10356/65738 |
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1759853039971205120 |