MODEL-BASED HARDWARE DESIGN FOR IMAGE PROCESSING SYSTEMS

Model-based design has been touted as the most viable design methodology of the future for the design of embedded hardware/software systems. Due to the large complex-ity of modern embedded systems, it is more and more error-prone to design systems with-out having a formal model to support and verify the application at design time. Also, formal models generally capture broad classes of applications, and thus any innovation on a modeling technique has the potential to enhance every individual application in the asso-ciated class. Often, a formal model captures the high-level abstraction of an application, which is lost in the final implementation, and thus modeling gives an effective platform to perform high-level design optimizations. Dataflow graphs have been widely used as for-mal models in the signal processing domain for a long time, and various commercial tools have adopted dataflow semantics for model-based design methodology. In this thesis, we develop a new dataflow meta-modeling technique, called homoge-neous parameterized dataflow (HPDF). HPDF is a meta-modeling technique in that it can be applied to a variety of underlying dataflow models of computation to enhance their expressive power, while maintaining much of the useful structure of the underlying mod