基于猴群算法的3D NoC IP核测试优化方法

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英文篇名：IP Cores Test Optimization Method of 3D NoC Based on Monkey Algorithm 作者：许川佩 ; 陈玄 英文作者：XU Chuan-pei;CHEN Xuan;School of Electronic Engineering and Automation,Guilin University of Electronic Technology;Guangxi Key Laboratory of Automatic Detection Technology and Instrument; 关键词：三维片上网络 ; IP核测试优化 ; 猴群算法 英文关键词：three-dimensional network-on-chip(3DNoC);;IP core test optimization;;monkey algorithm 中文刊名：WXYJ 英文刊名：Microelectronics & Computer 机构：桂林电子科技大学电子工程与自动化学院;广西自动检测技术与仪器重点实验室; 出版日期：2019-01-05 出版单位：微电子学与计算机 年：2019 期：v.36;No.416 基金：国家自然科学基金(61561012) 语种：中文; 页：WXYJ201901005 页数：6 CN：01 ISSN：61-1123/TN 分类号：28-32+37

摘要

如何对三维片上网络(three Dimensional Network-on-Chip,3DNoC)资源内核的测试进行优化以缩短测试时间,提高资源利用率是当前3DNoC测试面临的主要问题之一.本文针对3DNoC IP核测试优化问题,开展TSV位置与IP核测试数据分配方案协同优化研究.在带宽、功耗和TSV数量约束下,将TSV位置方案和IP核测试数据分配方案作为寻优变量,采用猴群算法进行寻优.算法通过爬和望跳过程进行局部搜索并结合翻过程在不同领域进行搜索从而找到最优解,加入精英保留策略以确保算法收敛性,使算法搜索结果更为准确.以ITC’02电路为实验对象,实验结果表明,该算法能够有效地优化3DNoC资源分配,缩短测试时间,提高资源利用率.
        How to optimize the test of the three-dimensional network-on-chip(3DNoC)resource core to shorten test time and increase resource utilization is one of the major problems that the 3DNoC testing faced.This work focuses on 3DNoC IP core test optimization issues,and conducts collaborative optimization research on TSV location and IP core test data distribution schemes.Under the constraints of bandwidth,power consumption and TSV quantity,the TSV location scheme and IP core test data distribution scheme are used as optimization variables and Monkey Algorithm(MA)is used to optimize.The algorithm performs local search through the climb and watch-jump process and searches in different fields with the somersault process to find the optimal solution.The elite retention strategy is added to ensure the convergence of the algorithm and the algorithm search result is more accurate.Taking the ITC'02circuit as the experimental object,the experimental results show that the algorithm can effectively optimize the 3DNoC resource allocation,shorten the test time,and improve resource utilization.

引文

[1] Karmakar R,Agarwal A,Chattopadhyay S.Testing of 3D-stacked ICs with hard-and soft-dies-aparticle swarm optimization based approach[J].Vlsi Test Symposium,IEEE,2015:1-6.
    [2] Xiang D,Liu G,Chakrabarty K,et al.Thermal-aware test scheduling for NOC-based 3Dintegrated circuits[C]//International Conference on Very Large Scale Integration.[s.l],IEEE,2013:96-101.
    [3]欧阳一鸣,贺超,梁华国,等.NoC架构下异构IP核的并行测试方法[J].电子学报,2013,41(12):2391-2396.
    [4]许川佩,刘洋,莫玮.带分复用的三维片上网络测试规划研[J].仪器仪表学报,2015,35(9):2120-2128.
    [5] Zhao R,Tang W.Monkey algorithm for global numerical optimization[J].Journal of Uncertain Systems,2008,2(3):165-176.
    [6] Jia J,Feng S,Liu W.A triaxial accelerometer monkey algorithm for optimal sensor placement in structural health monitoring[J].Measurement Science&Technology,2015,26(6):65104-65115.
    [7]许川佩,凌景,胡聪.动态带分复用的三维片上网络协同优化研究[J].仪器仪表学报,2016,37(12):2821-2828.
    [8]许川佩,陈家栋,万春霆.基于云模型进化算法的硅通孔数量受约束的3D NoC测试规划研究[J].电子与信息学报,2015,37(2):477-483.
    [9]许川佩,李克梅.基于粒子群算法的多约束3D NoC协同测试规划[J].仪器仪表学报,2017,38(3):765-772.