A structure-oriented power modeling technique for macrocells

To characterize the power consumption of a macrocell, a general method invo lves recording the power consumption of all possible input transition event s in the look-up tables. However, though this approach is accurate, the siz e of the table becomes very large. In this paper, we propose a new power mo deling technique that takes advantage of the structural information of a ma crocell, In this approach, a subset of primary inputs and internal nodes in the macrocell are selected as the state variables to build a state transit ion graph (STG), These state variables can model the steady-state transitio ns completely. Moreover, by selecting the characterization patterns properl y, the STG can also model the glitch power in the macrocell accurately. To further simplify the complexity of the STG, an incomplete power modeling te chnique is presented. Without losing much accuracy, the property of compati ble patterns is exploited for a macrocell to further reduce the number of e dges in the corresponding STG, Experimental results show that our modeling techniques can provide SPICE-like accuracy, while the size of the look-up t able is significantly reduced.