Efficient power modeling techniques for combinational and sequential RTL macroblocks

We propose efficient power macromodels for RTL combinational circuits, based only on word and bit level switching information of the module inputs. Combinations of these two properties for the construction of power macromodels are investigated. It is shown that our developed models reduce the estimation error compared to the commonly used word level switching model (Hamming-distance) at least by 64%. The total average errors achieved over a wide range of test modules and input stimuli are less than 4.6%. This is comparable to complex models based on several more signal properties. Finally, an extension of our methodology for sequential RTL modules is described.