Government Economic Management On Pattern Of Economical Development Transformation

Reconfigurable computing system is typically composed of general purpose processors and programmable devices, and it has limited hardware resource and software resource. Tasks can be implemented in software resource or hardware resource, but task execution time and power consumption may be significant difference. To take full advantage of hardware resource and to meet the demand scenarios, Hardware/software partitioning is required. Hardware/software partitioning is a key step and research focus in the design of reconfigurable computing system.Hardware/software partitioning is a combinatorial optimization problem, which has been proved to be NP-hard, the present study usually uses the heuristic algorithm to solve this problem, and the study focuses on improving the algorithm's convergence rate and solution quality. This paper presents improved heuristic algorithm to improving convergence rate and solution quality for two type sets of tasks.In the case of small and medium size set of tasks, this paper uses Simulated Annealing(SA) algorithm to solve hardware/software partitioning problem, which is a used heuristic algorithm, but SA has slow convergence speed and poor solution quality. So this paper uses an improved disturbance model and annealing schedule to improve its convergence speed. For algorithm's poor solution quality, this paper gives a new cost function. The new cost function guide the algorithm's search in the solution space, which avoid the blindness of the search, so the method can quickly find near optimal solutions and improve the solution quality.In the case of large size set of tasks, this paper combines both the improved greedy algorithm and SA to solve the hardware/software partitioning. In a large task set, the method which simply uses a heuristic algorithm to slove partitioning problem will lead to increased running time and poor solution. For these problems, firstly,this paper uses the improved greedy algorithm to initialize the task set. Greedy algorithm time complexity is low and easy to implement, and algorithm's solution can be close to the global approximate optimal solution region. Secondly, the SA is be used to continue the search. SA algorithm has global search ability, and can finally get the global approximate optimal solution.To validate the algorithm, this paper uses a common tool--TGFF to generate random test task set, and achieve the proposed algorithm and comparison algorithms in the same platform. Experiments show that the proposed algorithm has less running time than the comparison algorithms, and has improved solution quality in this small and middle scale of the task. In a large task set, the running time of the proposed algorithm, although has longer than the greedy algorithm, but has better solution quality; and the proposed algorithm produces better solutions and reduces the running time when compared to the SA.