基于Prim初始种群选取优化遗传算法的三维片上网络低功耗映射  

Low power mapping based on improved genetic algorithm with Prim initial population selection for 3D network-on-chip

作　　者：宋国治[1] 王铖[1,2] 涂遥[1] 张大坤[1] 

机构地区：[1]天津工业大学计算机科学与软件学院,天津300387 [2]云南大学信息学院,昆明650091

出　　处：《计算机应用》2017年第1期90-96,共7页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61272006);国家级大学生创新创业训练计划项目(201510058050)~~

摘　　要：针对将计算任务合理地映射到三维片上网络(NoC)的问题,提出了一种基于遗传算法(GA)的改进算法。GA具有快速随机的搜索能力,Prim算法可在加权连通图内得到最小生成树,改进算法结合了两种算法的优势,将计算任务合理地分配到各个网络节点,对于优化三维片上网络功耗和散热等问题具有很高的效率。通过仿真实验,对所提出的基于Prim算法的改进GA与基本GA的3D NoC映射算法进行了对比,仿真结果显示,基于Prim算法的改进GA平均功耗更低,从总体趋势来看,处理单元数量的增加与功耗降低幅度成正相关,在101个处理单元情况下,平均功耗比基本GA降低32%。To solve the problem of properly mapping the computational task onto a three-dimensional Network-on-Chip （NoC）, an improved algorithm based on Genetic Algorithm （GA） was proposed. GA has the fast random searching ability and Prim algorithm can get the minimal spanning tree of a weighted connected graph. By combining the two algorithms＇ advantages, the improved algorithm could properly assign computational tasks onto each network node, achieving a high efficiency on solving network power consumption and heat problems. The simulation experiments were carried out to compare the proposed improved GA based on Prim algorithm with GA based 3D NoC mapping algorithm. The simulation results indicate that the average power consumption of the improved GA based on Prim algorithm is lower： from the overall trend, the reduction on power consumption is positive correlated to the increase of the number of processing units, and when there are 101 processing units, the average power consumption is 32% lower than that of the traditional GA.

关 键 词：三维片上网络 低功耗 映射算法 遗传算法 PRIM算法 

分 类 号：TP393.01[自动化与计算机技术—计算机应用技术]