Novel modeling techniques for RTL power estimation

In this work, we propose efficient macromodeling techniques for RTL power estimation, based only on word and bit level switching information of the module inputs. We present practicable combinations of these two properties for the construction of power macromodels. It is demonstrated, that our developed models reduce the estimation error compared to the Hamming-distance model at least by 64%. The total average errors (compared to PowerMill) achieved over a wide range of test modules and input stimuli are less than 4.6%. This is comparable to complex models, which however, have to make use of several more signal properties.