Abstract

Complex software systems have a large number of choices in terms of selection of software components and hardware architectures for implementation. Modern embedded systems are becoming increasingly multifunctional. These design choices create a large space of possible design solutions called the design space. The design process requires exploring through this design space to find valid design solutions before the actual implementation. DSE is the critical design process in which system designs are modeled, evaluated and, eventually, optimized for the various extra-functional system behaviors. This paper presents, a methodology Framework for design space exploration using Fitness Prediction Techniques. The scenario-based DSE uses a multi-objective genetic algorithm (GA) to identifying the mapping with the best average quality. In order to keep the exploration of the scenario-based DSE efficient, fitness prediction is used to obtain the quality of a mapping. This fitness prediction is performed using a representative subset of application scenarios that is obtained using co-exploration of the scenario subset space. Larger subsets will obtain a similar accuracy, but the DSE will require more time to identify promising mappings that meet the requirements of multifunctional embedded systems. Computational tests show that the efficiency of design exploration technique

Description