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Title: Machine learning on FPGA CAD
Authors: Que, Yanghua
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2015
Abstract: 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.
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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