an obscured version of the register transfer language model 20 this simulation model 26 has state variables which are a function of state variables within the register transfer language model 20 an example of such a simulation model 26 is the gasp type model produced by arm limited whilst the register transfer language model 20 and the simulation model 26 are responding in parallel to the same stimulus signal data 22 a power modelling neural network 28 receives as one training input the determined power behaviour of the register transfer language model 22 from the power modelling tool 24 and as another training input data defining the state variables of the simulation model 26 that is extracted from the simulation model the power modelling neural network 28 will thus u201c learn u201d the relationship between state variable changes within the simulation model 26 and power behaviours to which they correspond this learnt power behaviour data 30 is captured in order that the power modelling neural network 28 may be used in a predictive way once its training is completed it will be appreciated that the power modelling neural network 28 will gradually increase in accuracy as it is able to observe larger and larger amounts of calculated power behaviour in response to a variety of stimulus signal data whilst a register transfer language model 20 has been discussed above it will be appreciated that this model could take a variety of forms such as a model which will be synthesised to provide a circuit layout a circuit layout itself.