Hybrid quantum probabilistic coding genetic algorithm for large scale hardware-software co-synthesis of embedded systems

Hardware-software co-synthesis is a key step of future design of embedded systems. It involves three interdependent subproblems: allocation of resources, assignment of tasks to resources, and scheduling the execution of tasks. Both assignment and scheduling are known to be NP-complete. So it is a really hard and challenging task to optimization algorithms. Both heuristic and evolutionary algorithms are commonly used in real world. Heuristic algorithms converge rapidly but often be trapped in local minima and evolutionary algorithms own high exploration capacity but become time-consuming when handling large-scale systems. In this paper, a new hybrid evolutionary algorithm, called Hybrid Quantum probabilistic coding Genetic Algorithm, is proposed to implement the co-synthesis of large scale multiprocessor embedded systems, in which a heuristic algorithm is combined with the Quantum probabilistic coding Genetic Algorithm to enhance the performance on the hard task. The experimental results show that HQGA has better performance than both HA and QGA on large scale HW/SW co-synthesis problems.