摘要
针对规模庞大的应用如何在No C平台上低功耗地运行,提出了一种基于改进遗传算法的片上网络低功耗映射方法。该方法利用任务节点的通信权重和映射平台的结构特征,对任务节点进行优先级划分并根据任务节点优先级及其连接关系获取较优初始映射解集。在此基础上,在遗传操作中加入轮盘转赌、最优邻居选择、进化逆转等操作,同时每次迭代中都以一定的概率选择初始解,防止算法停滞。实验结果表明,在相同任务模型和映射平台下,改进遗传算法对比于传统遗传算法和随机映射方法,都大幅度降低了功耗。
Focusing on the power decreasing of large-scale applications in network-on-chip,this paper proposed a modified genetic algorithm based method on low-power mapping. With communication weights of task nodes and structural features of mapping platform,this method acquired better initial mapping solution set with the consideration of task node priority and its connection. Moreover,it introduced the roulette wheel selection,best-neighbor selection and reverse evolution,and selected the initial solution with a certain probability at each iteration to prevent the algorithm stagnation. Experimental results show that,when maintaining the same task model and mapping platform,compared with the genetic algorithm and random mapping algorithm,our proposed algorithm greatly decreases the energy consumption.
出处
《计算机应用研究》
CSCD
北大核心
2016年第6期1862-1866,共5页
Application Research of Computers
基金
国家"973"计划资助项目(2012CB315904)
关键词
片上网络
低功耗
映射
改进遗传算法
结构特征
较优初始解
network-on-chip
low-power
mapping
modified genetic algorithm
structural feature
better initial solution
