基于赤池信息准则的低功耗应用最优概率模型选择

R. Chandramouli, V. Srikantam
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引用次数: 2

摘要

研究了低功耗VLSI应用中功率估计的最优概率模型选择。利用赤池信息准则对模拟功率数据估计混合密度模型的最优分量数。讨论了该算法的理论基础,并给出了ISCAS’85基准电路和大型工业电路的实验结果。结果表明,该方法对大型和小型电路都有良好的性能,即使观察到的样本数量很少。该算法有望作为预处理步骤,在应用任何其他功率估计程序之前自动计算出最优概率模型。我们还注意到该方法适用于超大规模集成电路中的其他模型选择问题。
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Optimum probability model selection using Akaike's information criterion for low power applications
Optimal probability model selection for power estimation in low power VLSI applications is studied. Akaike's information criterion is used to estimate the optimal number of components in a mixture density model for the simulated power data. Theory behind the proposed algorithm is discussed followed by experimental results for ISCAS '85 benchmark circuits and a large industrial circuit. The method is shown to perform well for both large and small circuits even when the number of observed samples is small. The algorithm is promising as a pre-processing step to automatically compute the optimal probability model before any other power estimation procedure is applied. We also note that the method is applicable to other problems in VLSI for model selection.
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