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Faster power estimation of CMOS designs using vector compaction - a fractal approach

机译:使用矢量压缩更快地估计CMOS设计的功率-分形方法

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Low power digital complementary metal oxide semiconductor (CMOS) circuit design requires accurate power estimation. In this paper, we present a compaction algorithm for generating compact vector sets to estimate power efficiently. Power can be estimated using dynamic (simulation) or static (statistical/probabilistic) techniques. Dynamic power estimation techniques simulate the design using a large input vector set for accurate estimation. However, the simulation time is prohibitively long for bigger designs with larger vector sets. The statistical methods, on the other hand, use analytical tools that make them faster but less accurate. To achieve the accuracy of dynamic power estimation and the speed of statistical methods, one approach is to generate a compact, representative vector set that has the same switching transition behavior as the original larger vector set. The compaction algorithm presented in this paper uses fractal concepts to generate such a compact vector set. The fractal technique quantifies correlation by a fractal parameter which can be determined faster than calculating correlation explicitly. Experimental results on circuits from the ISCAS85 and ISCAS89 benchmark suites, with correlated input vector sets, resulted in a maximum compaction ratio of 65.57X (average 38.14X) and maximum power estimation error of 2.4% (average 2.06%). Since the size of the compact vector set used for simulation is smaller, the simulation time will be shorter and will significantly speed up the design cycle.
机译:低功耗数字互补金属氧化物半导体(CMOS)电路设计需要准确的功耗估算。在本文中,我们提出了一种压缩算法,用于生成紧凑向量集以有效地估计功率。可以使用动态(模拟)或静态(统计/概率)技术估算功率。动态功率估算技术使用大型输入向量集来仿真设计,以进行精确估算。但是,对于具有较大矢量集的较大设计,仿真时间过长。另一方面,统计方法使用的分析工具可使它们更快但更不准确。为了实现动态功率估计的准确性和统计方法的速度,一种方法是生成一个紧凑的,具有代表性的向量集,该向量集具有与原始较大向量集相同的切换过渡行为。本文提出的压缩算法使用分形概念来生成这种压缩向量集。分形技术通过分形参数来量化相关性,该分形参数可以比明确计算相关性更快地确定。来自具有相关输入矢量集的ISCAS85和ISCAS89基准套件的电路的实验结果得出最大压缩比为65.57X(平均38.14X),最大功率估计误差为2.4%(平均2.06%)。由于用于仿真的紧凑向量集的大小较小,因此仿真时间将更短,并且将大大加快设计周期。

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