Power estimation for cycle-accurate functional descriptions of hardware

Cycle-accurate functional descriptions (CAFDs) are being widely adopted in integrated circuit (IC) design flows. Power estimation can potentially benefit from the inherent increase in simulation efficiency of cycle-based functional simulation. Currently, most approaches to hardware power estimation operate at the register-transfer level (RTL), or lower levels of design abstraction. Attempts at power estimation for functional descriptions have suffered from poor accuracy because the design decisions performed during their synthesis lead to an unavoidable, large uncertainty in any power estimate that is based solely on the functional description. We propose a methodology for CAFD power estimation that combines the accuracy achieved by power estimation at the structural RTL with the efficiency of cycle-accurate functional simulation. We achieve this goal by viewing a CAFD as an abstraction of a specific, known RTL implementation that is synthesized from it. We identify correlations between a CAFD and its RTL implementation, and "back-annotate" information into the CAFD solely for the purpose of power estimation. The resulting RTL-aware CAFD contains a layer of code that instantiates virtual placeholders for RTL components, and maps values of CAFD variables into the RTL components' inputs/outputs, thus enabling efficient and accurate power estimation. Power estimation is performed in our methodology by simply co-simulating the RTL-aware CAFD with a simulatable power model library that contains power macro-models for each RTL component. We present techniques to further improve the speed of CAFD power estimation, through the use of control state-based adaptive power sampling. We have implemented and evaluated the proposed techniques in the context of a commercial C-based hardware design flow. Experiments with a number of large industrial designs (up to 1 million gates) demonstrate that the proposed methodology achieves accuracy very close to RTL power estimation with two-to-three orders of magnitude speedup in estimation times.