  Existing power modelling research focuses on the model rather than the
process for developing models. An automated power modelling process that can be
deployed on different processors for developing power models with high accuracy
is developed. For this, (i) an automated hardware performance counter selection
method that selects counters best correlated to power on both ARM and Intel
processors, (ii) a noise filter based on clustering that can reduce the mean
error in power models, and (iii) a two stage power model that surmounts
challenges in using existing power models across multiple architectures are
proposed and developed. The key results are: (i) the automated hardware
performance counter selection method achieves comparable selection to the
manual method reported in the literature, (ii) the noise filter reduces the
mean error in power models by up to 55%, and (iii) the two stage power model
can predict dynamic power with less than 8% error on both ARM and Intel
processors, which is an improvement over classic models.
