PowPrediCT: Cross-Stage Power Prediction with Circuit-Transformation-Aware Learning
Yufan Du, Zizheng Guo, Xun Jiang, Zhuomin Chai, Yuxiang Zhao, Yibo Lin, Runsheng Wang, Ru Huang
Abstract
Accurate and efficient power analysis at early VLSI design stages is critical for effective power optimization. It is a promising yet challenging task to model the circuit power at early design stages, especially during placement with the clock tree and final signal routing unavailable. Additionally, optimization-induced circuit transformations like circuit restructuring and gate sizing can invalidate fine-grained power supervision. Addressing these difficulties, we introduce the first circuit-transformation-aware power prediction model at placement stage with robust generalization capabilities. Our technology includes a dedicated clock tree model and an innovative train-and-calibrate scheme that effectively integrates topological and layout features. Compared to the cutting-edge commercial IC engine Innovus, we have significantly reduced the cross-stage power analysis error between placement and detailed routing.