Theoretical analysis of word-level switching activity in the presence of glitching and correlation

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 α_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