Multi-Objective Design Space Exploration of Embedded System Platforms
Jan Busk Madsen, Thomas Stidsen, Peter Kjaerulf, Shankar Mahadevan · 2007
In this paper we present a multi-objective genetic algorithm to solve the problem of mapping a set of task graphs onto a heterogeneous multiprocessor platform. The objective is to meet all real-time deadlines subject to minimizing system cost and power consumption, while staying within bounds on local memory sizes and interface buffer sizes. Our approach allows for mapping onto a fixed platform or onto a flexible platform where architectural changes are explored during the mapping. We demonstrate our approach through an exploration of a smart phone, where five task graphs with a total of 530 tasks after hyper period extension are mapped onto a multiprocessor platform. The results show four non-inferior solutions which tradeoffs the various objectives. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.