Characterization-Free Behavioral Power Modeling

Alessandro Bogliolo, Luca Benini, Giovanni De Micheli

Abstract: We propose a new approach to RT-level power modeling for combinational macros, that does not require simulation-based characterization. A pattern-dependent power model for a macro is analytically constructed using only structural information about its gate-level implementation. The approach has three main advantages over traditional techniques: (i) it provides models whose accuracy does not depend on input statistics, (ii) it offers a wide range of tradeoff between accuracy and complexity, and (iii) it enables the construction of pattern-dependent conservative upper bounds.