Hardware/software partitioning utilizing Bayesian belief networks
AuthorOlson, John Thomas
AdvisorRozenblit, Jerzy W.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractIn heterogeneous systems design, partitioning of the functional specifications into hardware and software components is an important procedure. Often, a hardware platform is chosen and the software is mapped onto the existing partial solution, or the actual partitioning is performed in an ad hoc manner. The partitioning approach presented here is novel in that it uses Bayesian Belief Networks (BBNs) to categorize functional components into hardware and software classifications. The BBN's ability to propagate evidence permits the effects of a classification decision made about one function to be felt throughout the entire network. In addition, because BBNs have a belief of hypotheses as their core, a quantitative measurement as to the correctness of a partitioning decision is achieved. In this research, the motivation and background material are presented first. Next, a methodology for automatically generating the qualitative, structural portion of BBN, and the quantitative link matrices is given. Lastly, a case study of a programmable thermostat is developed to illustrate the BBN approach. The outcomes of the partitioning process are discussed and placed in a larger design context, called model-based Codesign.
Degree ProgramGraduate College
Electrical and Computer Engineering