%0 Thesis %1 th:meeuws07 %A Meeuws, R. J. %C Delft, Netherlands %D 2007 %K hardware model myown partitioning quantitative software %N CE--2007--02 %P 151 %T A Quantitative Model for Hardware/Software Partitioning %X 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 differs per hardware characteristic. We show that for flip-flops the mean error for the predictions is 69%. In conclusion, our quantitative model can make fast and sufficiently 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.

@mastersthesis{th:meeuws07, 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 differs per hardware characteristic. We show that for flip-flops the mean error for the predictions is 69%. In conclusion, our quantitative model can make fast and sufficiently 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.}, added-at = {2011-04-29T13:30:41.000+0200}, address = {Delft, Netherlands}, author = {Meeuws, R. J.}, biburl = {https://www.bibsonomy.org/bibtex/29bbd0cf4bd2149c8077bb2c7848161e0/roelmeeuws}, institution = {Delft University of Technology, Delft, Netherlands}, interhash = {d1bc7e769d3c99659b648aba0b52a5c4}, intrahash = {9bbd0cf4bd2149c8077bb2c7848161e0}, keywords = {hardware model myown partitioning quantitative software}, month = may, number = {CE--2007--02}, pages = 151, school = {Delft University of Technology, Delft, Netherlands}, timestamp = {2012-07-10T18:05:32.000+0200}, title = {A Quantitative Model for Hardware/Software Partitioning}, year = 2007 }