A Fast Cross-Layer Dynamic Power Estimation Method by Tracking Cycle-Accurate Activity Factors With Spark Streaming

Abstract

The advent of autonomous power-limited systems poses a new challenge for early design space exploration. The existing architecture-level power evaluation tools lose accuracy due to ignoring features of circuit-level behaviors and influences of process, voltage, and temperature variations. Although power estimations based on SPICE or PrimeTime PX (PTPX) are accurate enough, they come at the cost of long simulation time and are available only in very late phases of design flow. In this article, a fast and accurate dynamic power evaluation method is proposed, which estimates activity factors at the circuit level. The impact of process variation at the gate level is considered through the proposed effective capacitance model. Activity factors are then estimated by the model and input vectors of the circuit. Input vectors are generated by architecture-level simulations in the form of streaming. For real-time and high-speed power evaluation, a data streaming framework is proposed for massive parallelism. The cross-layer estimation is verified based on the functional units of PULPino processor running SPEC CPU2006 benchmarks. Compared with the SPICE results using SMIC 28-nm PDK, our cycle-by-cycle dynamic power analysis shows an average error of 5.4%. Meanwhile, our approach realizes 65.2% faster than the traditional PTPX simulation and 48.8% faster compared with the state-of-art cross-level evaluation method.