Algorithms for Estimating Number of Glitches and Dynamic Power in CMOS Circuits with Delay Variations

Jins D. Alexander, V. Agrawal
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引用次数: 11

Abstract

Dynamic power dissipation of a CMOS VLSI circuit depends on the signal activity at gate outputs. The activity includes the steady-state logic transitions as well as glitches. The latter are a function of gate delays, which, for modern VLSI circuits, have wide process-related variations. Both average and peak power dissipation are useful and are traditionally estimated by Monte Carlo simulation. This is expensive and the accuracy, especially for peak power,depends upon the number of circuit delay samples that are simulated. We present an alternative. We use zero-delay simulation of a vector pair to determine the steady-state logic activity. We derive linear-time algorithms that, using delay bounds for gates, determine the maximum, minimum and average number of transitions that each gate output can produce. From this information, we estimate the average and peak energy consumed by each vector pair in a given vector set. For a set of random vectors applied to c7552 circuit, our analysis determined the per-vector energy consumption as 82.2 picojoules average and 196.3 picojoules peak. In comparison, Monte Carlo simulation of 1,000 circuit samples gave 82.8 picojoules average and 146.1 picojoules peak. The discrepancy of the peak consumption will reduce if more samples were simulated in the Monte Carlo method. Even with 1,000 samples the CPU time of the Monte Carlo analysis was three orders of magnitude greater than the alternative method we offer in this paper.
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具有延迟变化的CMOS电路中故障数和动态功率估计算法
CMOS VLSI电路的动态功耗取决于门输出端的信号活度。该活动包括稳态逻辑转换和故障。后者是门延迟的函数,对于现代VLSI电路来说,门延迟具有广泛的与工艺相关的变化。平均和峰值功耗都是有用的,传统上是通过蒙特卡罗模拟来估计的。这是昂贵的,而且精度,特别是峰值功率,取决于模拟的电路延迟样本的数量。我们提出了另一种选择。我们使用向量对的零延迟模拟来确定稳态逻辑活动。我们推导线性时间算法,使用门的延迟界,确定每个门输出可以产生的最大,最小和平均转换数。根据这些信息,我们估计了给定向量集中每个向量对消耗的平均能量和峰值能量。对于应用于c7552电路的一组随机矢量,我们的分析确定了每个矢量的能量消耗为平均82.2皮焦耳和峰值196.3皮焦耳。相比之下,蒙特卡罗模拟的1000个电路样品得到平均82.8皮焦耳和峰值146.1皮焦耳。蒙特卡罗方法模拟的样本越多,峰值消耗的差异就越小。即使有1,000个样本,蒙特卡罗分析的CPU时间也比我们在本文中提供的替代方法大三个数量级。
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