基于云量子进化算法的NoC资源内核测试优化研究

Research on Optimal Test of NoC Resource Cores Based on Cloud Quantum Evolution Algorithm

针对片上网络中资源内核数量不断增多,提出了一种基于云量子进化算法优化选取测试端口对资源内核进行并行测试的方法,以降低资源内核测试时间.首先用云模型对量子进化算法进行改进;然后在片上网络测试功耗限制下确定测试端口对数,利用云量子进化算法优化选取最优端口位置,实现对资源内核的并行测试;此方法可以有效地减少测试时间,且网络规模越大效果越好;同时,与量子进化算法相比,云量子进化算法有更好的稳定性.

For the increasing number of the NoC resources cores, a method of NoC resources cores parallel test with cloud quantum evolutionary algorithm optimally selecting test ports was proposed, in order to reduce the test time of these cores.＂ Firstly, improving quantum evolution algorithm with cloud model is proposed. Then the method is presented to determin the maximum of test ports with system limited power, select the most optimal location of ports with cloud quantum evolutionary algorithm, and test cores in parallel through multi test ports. The method is effective to reduce the test time of NoC resource cores, and the effect will be better in larger network; at the same time, compared with quantum evolutionary algorithm, the cloud quantum evolutionary algorithm has better stability.