Abstract: Based on power and signal statistics analysis,this paper chooses Bayesian inference method to build cycle-accurate power macro-model.Through analyzing the influence of signal features on power,we choose input signal probability,input transition density and output transition density as3D coefficients used in Bayesian inference,and prove those coefficients can denote the temporal and spatial correlation information of signals.The experimental results indicate that the use of the proposed model results in significant （400＋ times） speed-ups in power estimation time,while average error in each cycle can be limited to10％.