| 朱望纯,周甜,胡聪,许川佩,朱爱军.基于正弦余弦算法的NoC测试规划研究[J].电子测量与仪器学报,2017,31(8):1178-1182 |
| 基于正弦余弦算法的NoC测试规划研究 |
| Test scheduling research for network on chip based on sine cosine algorithm |
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| DOI：10.13382/j.jemi.2017.08.003 |
| 中文关键词: 片上网络 测试规划 正弦余弦算法 优化 |
| 英文关键词:Network on Chip test scheduling sine cosine algorithm optimization |
| 基金项目:国家自然科学基金(61561012, 61662018)、广西自动检测技术与仪器重点实验室(YQ16106)、广西高校科学技术研究项目(KY2015YB110)、广西中青年教师基础能力提升项目(2017KY0210)资 |
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| Author | Institution |
| Zhu Wangchun | School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China |
| Zhou Tian | School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China |
| Hu Cong | 1. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China;
2. School of MechanoElectronic Engineering, Xidian University, Xi’an 710071, China |
| Xu Chuanpei | School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China |
| Zhu Aijun | School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China |
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| 摘要点击次数: 2976 |
| 全文下载次数: 16050 |
| 中文摘要: |
| 如何实现多约束条件下测试时间优化是目前片上网络(NoC)测试中亟待解决的问题。提出一种基于正弦余弦算法(SCA)的NoC测试规划优化方法。采用专用TAM的并行测试方法，在满足功耗、引脚约束的条件下，建立测试规划模型，对NoC进行测试。通过群体围绕最优解进行正弦、余弦的波动，以及多个随机算子和自适应变量进行寻优，达到最小化测试时间的目的。在ITC’02 test benchmarks测试集上进行对比实验，结果表明相比粒子群优化(PSO)算法，提出的算法能够获得更短的测试时间。 |
| 英文摘要: |
| How to optimize the test time under multiple constraints is an urgent problem to be solved in the network on chip (NoC) testing. An optimization method of NoC test scheduling based on sine cosine algorithm (SCA) is proposed. A parallel test method using dedicated test access mechanism (TAM) is adopted, and a test scheduling model for NoC is built to satisfy the power consumption and pin constraints. To achieve test time minimization, the population fluctuation with the sine and the cosine function around the optimal solution, and a group of random operators and adaptive variables are adopted. Comparing experiments on the ITC’02 test benchmarks test show that the proposed algorithm can achieve shorter test time than that of the particle swarm optimization (PSO) algorithm. |
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