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The Use of Genetic Algorithm to Derive Correlation Between Test Vector and Scan Register Sequences and Reduce Power Consumption

机译:利用遗传算法推导测试向量与扫描寄存器序列之间的相关性并降低功耗

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In most of existing approaches, the reorganization of test vector sequence and reordering scan chains registers to reduce power consumption are solved separately, they are seen as independent procedures. In the paper it is shown that a correlation between these two processes and strong reasons to combine them into one procedure run concurrently exist. Based on this idea, it is demonstrated that search spaces of both procedures can be combined together into a single search space in order to achieve better results during the optimization process. The optimization over the united search space was tested on ISCAS85, ISCAS89 and ITC99 benchmark circuits implemented by means of CMOS primitives from AMI technological libraries. Results presented in the paper show that lower power consumption can be achieved if the correlation is reflected, i.e., if the search space is united rather than divided into separate spaces. At the end of the paper, results achieved by genetic algorithm based optimization are presented, discussed and compared with results of existing methods.
机译:在大多数现有方法中,分别解决测试向量序列的重组和对扫描链寄存器进行重新排序以降低功耗的问题,它们被视为独立的过程。本文表明,这两个过程之间存在相关性,并且有很强的理由将它们组合为一个同时运行的过程。基于此思想,证明了可以将两个过程的搜索空间组合到一个搜索空间中,以便在优化过程中获得更好的结果。在通过AMI技术库中的CMOS原语实现的ISCAS85,ISCAS89和ITC99基准电路上,测试了统一搜索空间上的优化。该论文提出的结果表明,如果反映了相关性,即,如果搜索空间是统一的而不是分成单独的空间,则可以实现较低的功耗。最后,介绍,讨论并与基于遗传算法的优化方法取得的结果进行比较,并将其与现有方法的结果进行比较。

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