摘要
3D NoC映射通常涉及大量IP核及节点,使传统映射算法效率较低.为减少映射算法的执行时间,提高其优化能力,在传统蚁群算法（ACA）的基础上,提出一种动态蚁群算法（DACA）.该算法采用逻辑斯蒂S形函数的变化形式,在每轮迭代开始前,依据当前迭代次数动态调整参数α,β及蚂蚁总数M.实验结果表明,与ACA相比,DACA可以缩短执行时间,提高算法性能;在面向随机任务时,其单位时间优化能力可以提升38.2%~65.9%;而当面向多媒体系统的真实应用时,其单位时间优化能力可以提升25.3%~32.7%.
Considering that large number of nodes and tasks are involved during the mapping process of 3D NoC,traditional mapping algorithms are inefficient.To save the execution time and improve the optimization capacity,the dynamic ant colony algorithm（DACA） is proposed based on the ant colony algorithm（ACA） in this paper.In DACA,parameters α,β and the ant number M are all adjusted dynamically during the iteration by the logistic sigmoid function.Experimental results show that DACA can save the execution time and improve the performance compared with ACA.For random generated tasks,the improvement of the optimization capacity per second can reach 38.2%~65.9%.And,for a multimedia system,it can achieve an improvement of 25.3%~32.7%.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2011年第9期1614-1620,共7页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(60876017
61006018)
中央高校基本科研业务费专项资金资助(1095021031)
江苏省科技支撑计划项目(BE2009143)
江苏省产学研前瞻性联合研究项目(BY2009146)
江苏省普通高校研究生科研创新计划资助项目(CX10B_021Z)
关键词
3D片上网络
映射
动态蚁群算法
3D network on chip
mapping
dynamic ant colony algorithm
