  We introduce and experimentally validate a new macro-level model of the CPU
temperature/power relationship within nanometer-scale application processors or
system-on-chips. By adopting a holistic view, this model is able to take into
account many of the physical effects that occur within such systems. Together
with two algorithms described in the paper, our results can be used, for
instance by engineers designing power or thermal management units, to cancel
the temperature-induced bias on power measurements. This will help them gather
temperature-neutral power data while running multiple instance of their
benchmarks. Also power requirements and system failure rates can be decreased
by controlling the CPU's thermal behavior.
  Even though it is usually assumed that the temperature/power relationship is
exponentially related, there is however a lack of publicly available physical
temperature/power measurements to back up this assumption, something our paper
corrects. Via measurements on two pertinent platforms sporting nanometer-scale
application processors, we show that the power/temperature relationship is
indeed very likely exponential over a 20{\deg}C to 85{\deg}C temperature range.
Our data suggest that, for application processors operating between 20{\deg}C
and 50{\deg}C, a quadratic model is still accurate and a linear approximation
is acceptable.
