Considering Process Variations During System-Level Power Analysis

Process variations will increasingly impact the operational characteristics of integrated circuits in nanoscale semiconductor technologies. Researchers have proposed various design techniques to address process variations at the mask, circuit, and logic levels. However, as the magnitude of process variations increases, their effects will need to be addressed earlier in the design cycle. In this paper, we propose techniques for accurately and efficiently incorporating the effects of process variations into system-level power estimation tools. To motivate our work, we first study the impact of process variations on the power consumption of an example system-on-chip (SoC). We consider simple extensions of current approaches to system-level power estimation (spreadsheet-based and simulation-based power estimation), and demonstrate their limitations in performing variation-aware power estimation. We propose a system-level power estimation methodology that can accurately and efficiently analyze the impact of process variations on SoC power. The proposed methodology combines efficient trace-based analysis, power-state based leakage modeling, and Monte Carlo sampling. The key benefit of the proposed methodology is that it captures the necessary inter-dependencies while avoiding iterative system-level simulation. Our implementation of the proposed techniques within an in-house system-level power estimation framework indicates 2-5 orders of magnitude efficiency gains, with negligible loss in accuracy, compared to direct Monte Carlo techniques that require iterative system simulation