Preference-driven hierarchical hardware/software partitioning

We present a hierarchical evolutionary approach to hardware/software partitioning for real-time embedded systems. In contrast to most previous approaches, we apply a hierarchical structure and dynamically determine the granularity of tasks and hardware modules to adaptively optimize the solution while keeping the search space as small as possible. Two new search operators are described, which exploit the proposed hierarchical structure. Efficient ranking is another problem addressed. Imprecisely specified multiple attribute utility theory has the advantage of constraining the solution space computation overhead. We propose a new technique to reduce the overhead. Experiment results show that our algorithm is both effective and efficient