Mapping of applications on a Multiprocessor System-on-Chip (MP-SoC) is a crucial step to optimize performance, energy and memory constraints at the same time. The problem is formulated as finding solutions to a cost function of the algorithm performing mapping and scheduling under strict constraints. Our solution is based on simultaneous optimization of execution time and memory consumption whereas traditional methods only concentrate on execution time. Applications are modeled as static acyclic task graphs that are mapped on MP-SoC with customized simulated annealing. The automated mapping in this paper is especially purposed for MP-SoC architecture exploration, which typically requires a large number of trials without human interaction. For this reason, a new parameter selection scheme for simulated annealing is proposed that sets task mapping specific optimization parameters automatically. The scheme bounds optimization iterations to a reasonable limit and defines an annealing sche...
Heikki Orsila, Tero Kangas, Erno Salminen, Timo D.