Dynamic power estimation for deep submicron circuits with process variation

Dynamic power consumption in CMOS circuits is usually estimated based on the number of signal transitions. However, when considering glitches, this is not accurate because narrow glitches consume less power than wide glitches. Glitch width and transition density modeling is further complicated by the effect of process variation. This paper presents a fast and accurate dynamic power estimation method that considers the detailed effect of process variation. First, we extend the probabilistic modeling approach to handle timing variations. Then the power consumption of a logic gate is computed based on the transition waveforms of its inputs. Both mean values and standard deviations of the dynamic power are estimated with high confidence based on accurate device characterization data. Compared with SPICE-based Monte Carlo simulations for small circuits, our power estimator reports power results within 3% error for the mean and 5% error for the standard deviation with six orders of magnitude speedup. For medium and large benchmarks, it is impossible to run Monte Carlo simulations with enough samples due to very long runtime, while our estimator can finish within minutes.