This paper gives an overview of the current state of ASoC design
methodology and presents preliminary results on evaluating the learning
classifier system XCS for the control of a QuadCore. The ASoC design
methodology can determine system reliability based on activity, power and
temperature analysis, together with reliability block diagrams. The
evaluation of the XCS shows that in the evaluated setup, XCS can find
optimal operating points, even in changed environments or with changed
reward functions. This even works, though limited, without the genetic
algorithm the XCS uses internally. The results motivate us to continue
the evaluation for more complex setups
