Print Email Facebook Twitter A Quantitative Model for Hardware/Software Partitioning Title A Quantitative Model for Hardware/Software Partitioning Author Meeuws, R.J. Contributor Bertels, K.L.M. (mentor) Faculty Electrical Engineering, Mathematics and Computer Science Department Microelectronics & Computer Engineering Programme Computer Engineering Date 2007-05-03 Abstract Heterogeneous System Development needs Hardware/Software Partitioning performed early on in the development process. In order to do this early on predictions of hardware resource usage and delay are necessary. In this thesis a Quantitative Model is presented that can make early predictions to support the partitioning process. The model is based on Software Complexity Metrics, which capture important aspects of functions like control intensity, data intensity, code size, etc. In order to remedy the interdependence of the software metrics a Principal Component Analysis performed. The hardware characteristics were determined by automatically generating VHDL from C using the DWARV C-to-VHDL compiler. Using the results from the principal component analysis, the quantitative model was generated using linear regression. The error of the model di?ers per hardware characteristic. We show that for ?ip-?ops the mean error for the predictions is 69%. In conclusion, our quantitative model can make fast and su?ciently accurate area predictions to support Hardware/Software Partitioning. In the future, the model can be extended by introducing extra software metrics, using more advanced modeling techniques, and using a larger collection of functions and algorithms. Subject reconfigurable computingregression analysissoftware metricsC languagehardware description languagesprincipal component analysis To reference this document use: http://resolver.tudelft.nl/uuid:48b95ec8-a5aa-477d-8640-64a48d3d4fdf Part of collection Student theses Document type master thesis Rights (c) 2007 Meeuws, R.J. Files PDF 1309_553_thesis_.pdf 2.5 MB Close viewer /islandora/object/uuid:48b95ec8-a5aa-477d-8640-64a48d3d4fdf/datastream/OBJ/view