Asymmetrical and lower bounded support vector regression for power estimation

In an energy aware environment, designers frequently turn to advanced power reduction techniques such as power shutoff and multi-supply-voltage architectures. In order to implement these techniques, it is important that power estimates be made. Power prediction is a critical necessity as chip sizes continually decrease and the desire for low power consumption is a foremost design objective. For such predictions, it is crucial to avoid underestimating power since reliability issues and possible chip damage might occur. It becomes necessary to eliminate or strictly limit underestimations by relaxing accuracy constraints while decreasing the likelihood that the estimation undershoots the actual value. Our novel approach, Asymmetrical and Lower Bounded Support Vector Regression modifies the Support Vector Regression technique by Vapnik and provides accurate prediction while maintaining a low number of underestimates. We tested our approach on two different power data sets and achieved accuracy rates of 5.72% and 5.06% relative percentage error while keeping the number of underestimates below 2.81% and 1.74%.