This paper presents a novel analytical approach to complete the switching activity in digital circuits at the word-level in the presence of glitching and correlation. The proposed approach makes use of signal statistics such as mean, variance, and autocorrelation. A novel expression is derived for the switching activity /spl alpha//sub f/ at the output node f of an arbitrary circuit in terms of time-slot autocorrelation coefficient, the expected value, and the signal probability. The switching activity analysis of a signal at the word-level is computed by summing the activities of all the individual bits constituting the signal. A novel relationship between the correlation coefficient of the higher order bits of a normally distributed signal and the bit where the correlation begins is also presented. The proposed approach can estimate the switching activity in less than a second which is orders of magnitude faster than simulation based approaches. Simulation results show that Me errors using the proposed approach are about 6% on an average and that the approach is well suited even for highly correlated speech and music signals.
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机译：本文提出了一种新颖的分析方法，可以在存在毛刺和相关性的情况下在字级上完成数字电路中的开关活动。所提出的方法利用了诸如均值，方差和自相关之类的信号统计信息。根据时隙自相关系数，期望值和信号概率，推导了针对任意电路的输出节点f处的开关活动/ spl alpha // sub f /的新颖表达式。通过对构成信号的所有单个位的活动求和，可以计算出字级信号的切换活动分析。还提出了正态分布信号的高阶位的相关系数与相关开始处的位之间的新颖关系。所提出的方法可以在不到一秒钟的时间内估算出开关活动，这比基于仿真的方法要快几个数量级。仿真结果表明，使用所提出的方法的Me误差平均约为6％，并且该方法也非常适合于高度相关的语音和音乐信号。
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