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We present novel macromodeling techniques for estimating the energy dissipated and peak-current drawn in a logic circuit for every input vector pair (we call this the energy-per-cycle and peak-current-per-cycle, respectively). The macromodels are based on classifying the input vector pairs on the basis of their Hamming distances and using a different equation-based macromodel for every Hamming distance. The variables of our macromodel are the zero-delay transition counts at three logic levels inside the circuit. We present an automatic characterization process by which such macromodels can be constructed. The energy-per-cycle macromodel provides a transient energy waveform, and can also be used to estimate the moving average energy over any time window, whereas peak-current-per-cycle macromodel provides peak-current which can be used for studying IR drop problems. Some key features of this technique are: 1) the models are compact (linear in the number of inputs); 2) they can be used for any input sequence; and 3) the characterization is automatic and requires no user intervention. These approaches have been implemented and models have been built and tested for many circuits. The average errors observed in estimating the energy-per-cycle and peak-current-per-cycle are under 20%. The energy-per-cycle model can also be used to measure the long-term average power, with an observed error of under 10% on average.