基于动态自适应离散粒子群算法的3D NoC低功耗映射方法

刘勤让; 戴启华; 沈剑良; 赵博

doi:10.11959/j.issn.1000-436x.2016215

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基于动态自适应离散粒子群算法的3D NoC低功耗映射方法

学术论文 | 更新时间：2024-06-05

- 基于动态自适应离散粒子群算法的3D NoC低功耗映射方法
- Dynamic adaptive discrete particle swarm optimization algorithm based method on low-power mapping in network-on-chip
- 通信学报 2016年37卷第11期页码：23-30
- 作者机构：
  
  国家数字交换系统工程技术研究中心，河南郑州 450000
- 作者简介：
  
  [ "刘勤让（1975-），男，河南睢县人，博士，国家数字交换系统工程技术研究中心研究员，主要研究方向为系统体系结构、网络安全与芯片设计。" ]
  [ "戴启华（1990-），男，浙江湖州人，国家数字交换系统工程技术研究中心硕士生，主要研究方向为片上网络映射与芯片设计。" ]
  [ "沈剑良（1982-），男，浙江德清人，博士，国家数字交换系统工程技术研究中心助理研究员，主要研究方向为嵌入式系统、可重构计算与片上网络。" ]
  [ "赵博（1981-），男，吉林公主岭人，博士，国家数字交换系统工程技术研究中心助理研究员，主要研究方向为软件定义网络。" ]
- 基金信息：
  
  国家高技术研究发展计划（“863”计划）基金资助项目(2014AA01A704);国家自然科学基金创新群体基金资助项目(61521003);国家自然科学基金面上基金资助项目(61572520)
- DOI：10.11959/j.issn.1000-436x.2016215
  中图分类号： TP393.03
- 网络出版日期：2016-11，
  
  纸质出版日期：2016-11-25
- 稿件说明：
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刘勤让, 戴启华, 沈剑良, 等. 基于动态自适应离散粒子群算法的3D NoC低功耗映射方法[J]. 通信学报, 2016,37(11):23-30.

Qin-rang LIU, Qi-hua DAI, Jian-liang SHEN, et al. Dynamic adaptive discrete particle swarm optimization algorithm based method on low-power mapping in network-on-chip[J]. Journal on communications, 2016, 37(11): 23-30.
刘勤让, 戴启华, 沈剑良, 等. 基于动态自适应离散粒子群算法的3D NoC低功耗映射方法[J]. 通信学报, 2016,37(11):23-30. DOI： 10.11959/j.issn.1000-436x.2016215.

Qin-rang LIU, Qi-hua DAI, Jian-liang SHEN, et al. Dynamic adaptive discrete particle swarm optimization algorithm based method on low-power mapping in network-on-chip[J]. Journal on communications, 2016, 37(11): 23-30. DOI： 10.11959/j.issn.1000-436x.2016215.

摘要

相对于2D NoC

3D NoC具有更好的集成度和系统性能，是解决低功耗映射的一个可靠途径。在传统粒子群算法(PSOA

particle swarm optimization algorithm)的基础上，提出了一种动态自适应离散粒子群算法(DADPSOA

dynamic adaptive discrete particle swarm optimization algorithm)。该算法基于早熟收敛程度和个体适应度值变化动态调整参数ω，不断靠近最优解；同时对粒子进行合理的解构造，减小了算法时间复杂度。仿真结果表明，与随机映射、遗传算法(GA

genetic algorithm)、PSOA和动态蚁群算法(DACA

dynamic ant colony algorithm)相比，DADPSOA可以缩短执行时间，减小映射结果通信功耗；在面向任务图映射的时候，其通信功耗下降。

Abstract

Compared to 2D NoC

3D NoC has better integrated density and system performance

which was a reliable method to solve the problem about low-power mapping. On the basis of the traditional particle swarm optimization algo-rithm (PSOA)

a dynamic adaptive discrete particle swarm optimization algorithm (DADPSOA) was proposed . Parame-ter in this algorithm was adjusted dynamically based on the degree of early convergence and the charge of individual adap-tive value to approach the optimal solution. At the same time

the reasonable structure of the particles was made aiming at reducing the time complexity of this algorithm. Experimental results show that comparing with the random mapping

genetic algorithm (GA)

PSOA and dynamic ant colony algorithm (DACA)

DADPSOA can save the execution time

reduce the communication power consumption of mapping results. The power consumption of the task graph is reduced.

关键词

Keywords

references

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