中文摘要：

如何实现测试时间和测试功耗协同优化是目前片上网络（Network-on-Chip,NoC）测试中亟待解决的问题.提出一种基于调和距离量子多目标进化算法（Harmonic distance quantum-inspired multiobjective evolutionary algorithm,HQMEA）的NoC测试规划优化方法.采用重用NoC作为测试存取机制（Test access mechanism,TAM）的并行测试方法,对NoC中的内核进行测试,节省测试资源,提高测试效率.提出的算法在量子多目标进化算法（Quantum-inspired multiobjective evolutionary algorithm,QMEA）的基础上,采用多进制概率角编码替代二进制概率幅编码,更好的适应NoC测试规划问题;采用调和距离替代拥挤距离（Crowding distance）能更好的衡量拥挤程度;采用混沌策略动态更新旋转角,能很好地兼顾了算法的探索和发掘能力.在ITC’02test benchmarks测试集上进行对比实验,结果表明相比量子多目标进化算法,提出的算法不仅提升了算法的收敛性,而且保证了Pareto解集良好的分布性.

英文摘要：

Network-on-chip（NoC）is an emerging communication paradigm for the next-generation complex core-based system chips.The co-optimization of test time and test power consumption is currently an emergency problem to be solved for network-on-chip testing.In this paper,we propose a harmonic distance quantum-inspired multi-objective evolutionary algorithm（HQMEA）for NoC test scheduling optimization.The reuse of on-chip network as test access mechanism（TAM）in NoC relies on the reuse of existing resources without introducing new overhead,it can affords a cost-efficient solution to the NoC-based system testing.Therefore,for the sake of saving testing resources and impro-ving the test efficiency,we adopt the parallel test method of the NoC reuse to test the Intellectual Property（IP）cores of the NoC.On the basis of the quantum-inspired multiobjective evolutionary algorithm（QMEA）,the proposed algorithm adoptS multi-nary probability angle coding as an alternative to probability amplitude binary coding,which is suitable for the NoC test scheduling problem;Then,the proposed algorithm uses the harmonic distance as a represent of crowded distance to better measure crowded degree;In addition,the proposed algorithm adopts the strategy of chaos dynamically updating rotation angle to improve the balance between exploration and exploitation of the algorithm.The comparative experiments are conducted on the ITC＇02test benchmarks.The results show that compared with quantum multiobjective evolutionary algorithms,the proposed algorithm not only improves the convergence of the algorithm,but also ensures a better distribution on the Pareto front.It confirms the superiority of the proposed algorithm in solving multiobjective optimization problems.