改进模拟退火遗传算法的3D NoC低功耗映射

功耗优化是NoC设计的重要部分,针对将IP (intellectual property)核合理映射NoC的问题,提出一种初始种群优化的模拟退火遗传映射算法.首先以功耗优化为主要目标,通过对初始种群选取方法进行改进来获取功耗更低的映射方案,并针对遗传算法局部最优问题,在遗传算法交叉操作阶段结合模拟退火算法,得到全局最优方案.实验在Windows系统下采用C++语言实现,结果显示,与传统的遗传算法相比,该算法具有较好的收敛性,能快速搜索到较优解,在124个IP核的情况下,采用改进的模拟退火遗传算法进行映射产生的平均功耗比使用遗传算法时降低了32.0%.

Low Power Mapping Based on Improved Simulated Annealing Genetic Algorithm for 3D NoC

Power optimization is an important part in NoC design. In order to solve the problem of mapping IP cores to NoC reasonably, an improved simulated annealing genetic algorithm(ISAGA) based on initial population optimization is proposed in this paper. Firstly, the initial population selection method is improved to obtain a lower power consumption mapping scheme. Then for the local optimization problem of the genetic algorithm, the simulated annealing algorithm is combined with the genetic algorithm cross-operation stage to obtain the global optimal scheme. The experiment is implemented in C++ language under Windows system,the experimental results show that compared with the traditional genetic algorithm, the algorithm has better convergence and can quickly search for better solutions, in the case of 124 IP cores, the proposed method can reduce 32.0% compared with the genetic algorithm.