1. 桂林电子科技大学 机构 电子工程与自动化学院,广西,桂林,541004
2. 广西自动检测技术与仪器重点实验室,广西,桂林,541004
纸质出版:2016
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许川佩, 凌景. 基于ICEA的3D NoC测试规划研究[J]. 微电子学与计算机, 2016, 33(7): 74-78.
XU Chuan-pei, LING Jing. Test Scheduling for Three Dimensional Network-on-Chip Based on Improved Cloud Evolutionary Algorithm[J]. 2016, 33(7): 74-78.
采用带分复用的测试方法
结合自然进化策略的云进化算法(CEA)优化3D NoC测试规划
依据带分复用的特点建立3D NoC测试时间优化模型
在功耗和带宽双重约束下
采用改进的云进化算法(ICEA)对该模型进行求解
完成3D NoC测试.以ITC’02基准电路为实验对象
实验结果表明该算法能迅速收敛到适应值最佳的测试规划
实现3D NoC资源内核的最大化并行测试
提高测试效率
缩短测试时间.
This paper adopt the test method of bandwidth division multiplexing
based on cloud evolutionary algorithm that combined with the natural evolution strategies to optimize the test scheduling for 3D NoC. Establish test time model according to the features of bandwidth division multiplexing
under the dual constraints of power and bandwidth
the model is solved with improved cloud evolutionary algorithm to complete the test. With ITC02 test circuit as the simulation object
experimental results show this algorithm can search best test scheduling quickly
maximize the parallel test on 3D NoC resources cores
improve test efficiency and reduce testing time.
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Mukesh Agrawal, Krishnendu Chakrabarty. Test time optimization in NoC-based manycore SoCs using multicast routing[C]//2014 IEEE 32nd VLSI Test Symposium(VTS). California, IEEE Computer Society Press, 2014: 18-23.
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. DOI: 477
李国柱. 基于云模型的实数编码量子进化算法[J]. 计算机应用, 2013, 33(9): 2550-2552. DOI: 2550
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