A framework for estimating maximum power dissipation in CMOS combinational circuits using genetic algorithms

J. Placer, A. Sagahyroon, M. Massoumi
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引用次数: 4

Abstract

Assessing the maximum power dissipated by a CMOS combinational circuit is a complex problem because the power dissipated is input-pattern dependent. Simulation techniques are impractical, especially for large circuits, since the number of simulation runs needed increases exponentially with the number of inputs to the circuit. In this paper a genetic algorithm (GA) based approach is presented for generating a sequence of input vectors that tend to continuously maximize the switching activity of the circuit and hence the maximum power dissipated. The GA used evolves candidate input vectors while making use of a logic simulator to compute the fitness of each candidate. Experimentation with different GA parameters was carried out in order to derive an optimal set of working parameters for the GA. The performance of the GA technique was evaluated using "test circuits" whose topology allows simple analysis to determine the maximum number of simultaneous transitions possible for the circuits. In addition to this, some circuits from the ISAC-85 benchmark suite of circuits were also tested. The GA method was found to significantly out perform simulation-based techniques, especially in terms of CPU time expenditures.
一种利用遗传算法估计CMOS组合电路最大功率耗散的框架
评估CMOS组合电路的最大功率耗散是一个复杂的问题,因为耗散与输入模式有关。模拟技术是不切实际的,特别是对于大型电路,因为所需的模拟运行次数随着电路输入的数量呈指数增长。本文提出了一种基于遗传算法(GA)的方法,用于生成一系列输入向量,这些输入向量倾向于连续最大化电路的开关活动,从而最大化功耗。该遗传算法对候选输入向量进行演化,同时利用逻辑模拟器计算每个候选输入向量的适应度。在不同的遗传算法参数下进行了实验,得出了遗传算法的最优工作参数集。使用“测试电路”来评估遗传算法的性能,测试电路的拓扑结构允许简单的分析,以确定电路可能同时转换的最大数量。除此之外,还测试了ISAC-85基准电路套件中的一些电路。研究发现,遗传算法的性能明显优于基于仿真的技术,尤其是在CPU时间消耗方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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