Estimation of bit-level transition activity in data-paths based on word-level statistics and conditional entropy

Accurate models for the calculation of bit-level switching activity from word-level statistics are presented. The dual-bit type (DBT) model is combined with an entropy-based transition activity calculation. Assuming that the input signal of a DSP system is described by a random Gaussian process, the conditional entropy per bit is estimated by means of a novel sigmoidal model. An accurate polynomial approximation to the conditional entropy of the sign bits has been developed permitting the direct calculation of the switching activity. The propagation of signals through common data-path operators is studied. A triple-bit type (TBT) model is introduced to accurately describe the three activity regions of the multiplier output. Moreover, the proposed model is applied for the estimation of the switching activity at the nodes of a multiply-accumulate (MAC) architecture implementing a 24-tap FIR filter, for both synthetic and real data streams. The average error between measured and estimated values of the switching activity is less than 6% in all the cases encountered