摘要
针对硅通孔(TSV)价格昂贵、占用芯片面积大等问题,该文采用基于云模型的进化算法对TSV数量受约束的3维片上网络(3D No C)进行测试规划研究,以优化测试时间,并探讨TSV的分配对3D No C测试的影响,进一步优化3D No C在测试模式下的TSV数量。该方法将基于云模型的进化算法、小生境技术以及遗传算法的杂交技术结合起来,有效运用遗传、优胜劣汰以及保持群落的多样性等理念,以提高算法的寻优速度和寻优精度。研究结果表明,该算法既能有效避免陷入局部最优解,又能提高全局寻优能力和收敛速度,缩短了测试时间,并且优化了3D No C的测试TSV数量,提高了TSV的利用率。
As Through-Silicon-Vias（TSVs） in three-Dimensional Network-on-Chip（3D No C） accompany some overhead such as the cost and the area, in order to optimize the number of TSVs of 3D No C in test mode and reduce the test time, a new method using evolution algorithm based on cloud model is proposed to research on the test scheduling of 3D No C and the impact of TSVs number and their allocation in each layer on 3D No C test. This method combines the cloud evolution algorithm with niche technology and hybridization technique in genetic algorithm. It uses effectively the concepts of heredity, natural selection and community diversity to improve the quality of the algorithm on optimizing speed and precision. Experimental results demonstrate that the proposed method can not only effectively prevent from running into local optimization solution, but also improve the ability and speed of searching the best solution, and that TSVs number of 3D No C can be optimized to improve the TSVs＇ utilization.
出处
《电子与信息学报》
EI
CSCD
北大核心
2015年第2期477-483,共7页
Journal of Electronics & Information Technology
关键词
3维片上网络
硅通孔
云模型
进化算法
Three-Dimensional Network-on-Chip（3D No C）
Through-Silicon-Via（TSV）
Cloud model
Evolution algorithm
