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RTL-Aware Cycle-Accurate Functional Power Estimation

Most methods for hardware power estimation operate at the register-transfer level (RTL) or lower levels of design abstraction. Since 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 the efficiency of a cycle-based functional simulation. However, attempts at power estimation for functional descriptions have suffered from a 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. The authors propose a methodology for a CAFD power estimation that combines the accuracy achieved by structural RTL power estimation with the efficiency of cycle-accurate functional simulation. This goal is achieved by viewing a CAFD as an abstraction of a specific known RTL implementation that is synthesized from it. The authors identify correlations between a CAFD and its RTL implementation and "back-annotate" information into the CAFD solely for 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 the proposed methodology by simply cosimulating the RTL-aware CAFD with a simulatable power-model library that contains power macromodels for each RTL component. Techniques to further improve the speed of CAFD power estimation through the use of control-state-based adaptive power sampling are presented. The authors 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 1 000 000 gates) demonstrate that the proposed methodology achieves an accuracy very close to RTL power estimation with two-three orders of magnitude speedup in estimation times