摘要:
Modern embedded systems must increasingly accommodate dynamically changing operating environments, high computational requirements, exibility (e.g., for the emer- gence of new standards and services), and tight time-to-market windows. Such trends and the ever-increasing design complexity of embedded systems have challenged design- ers to raise the level of abstraction and replace traditional ad-hoc approaches with more efcient synthesis techniques. Additionally, since embedded multiprocessor systems are typically designed as nal implementations for dedicated functions, modications to em- bedded system implementations are rare, and this allows embedded system designers to spend signicantly larger amounts of time to optimize the architecture and the employed software. This dissertation presents several system-level synthesis algorithms that employ thorough and hence time-intensive optimization techniques (e.g. evolutionary algorithms) that allow the designer to explore a signicantly larger part of the design space. It looks at critical issues that are at the core of the synthesis process selecting the architecture, partitioning the functionality over the components of the architecture, and scheduling ac- tivities such that design constraints and optimization objectives are satised. More specically for the scheduling step, a new solution to the two-step (cluster- ing and cluster-merging) multiprocessor scheduling problem is proposed. For the rst step or pre-processing step of clustering a simple yet highly efcient genetic algorithm is proposed. Several techniques for the second step of merging or cluster scheduling are proposed and nally a complete two-step effective solution is presented. Also, a randomization technique is applied to existing deterministic techniques to extend these techniques so that they can utilize arbitrary increases in available optimization time. This novel framework for extending deterministic algorithms in our context allows for accurate and f