Power modelling of a circuit

Abstract

Stimulation signals () are applied to a first circuit model () and the power behaviour of the circuit being modelled is determined from the behaviour of the first circuit model (). In parallel, the same stimulation signals () are applied to a second circuit model () and the state variable changes within that second circuit model are calculated. The calculated power behaviour and the calculated state variable changes are then applied as training data inputs to a self learning power model, such as a neural network (), which learns the relationship between state variable changes between the second model () and power behaviour of the circuit being simulated. In this way, a detailed first circuit model () may be used to calculate power behaviour and to train a separate power model () which once trained can be publicly released without having to release sensitive information within the first circuit model ().