This paper presents an application-specific, heterogeneous multiprocessor synthesis system, named HeMPS, that combines a form of Evolutionary Computation known as Differential Evolution with a scheduling heuristic to search the design space efficiently. We demonstrate the effectiveness of our technique by comparing it to similar existing systems. The proposed strategy is shown to be faster than recent systems on large problems while providing equivalent or improved final solutions.

We present a design strategy to reduce power demands in application-specific, heterogeneous multiprocessor systems with interdependent subtasks. This power reduction scheme can be used with a randomised search such as a genetic algorithm where multiple trial solutions are tested. The scheme is applied to each trial solution after allocation and scheduling have been performed. Power savings are achieved by equally expanding each processor's execution time with a corresponding reduction in their respective operating voltage. Lowest cost solutions achieve average reductions of 24% while minimum power solutions average 58%.