3DIC benefit estimation and implementation guidance from 2DIC implementation

Quantification of three-dimensional integrated circuit (3DIC) benefits over corresponding 2DIC implementation for arbitrary designs remains a critical open problem, largely due to nonexistence of any “golden” 3DIC flow. Actual design and implementation parameters and constraints affect 2DIC and 3DIC final metrics (power, slack, etc.) in highly non-monotonic ways that are difficult for engineers to comprehend and predict. We propose a novel machine learning-based methodology to estimate 3DIC power benefit (i.e., percentage power reduction) based on corresponding golden 2DIC implementation parameters. The resulting 3D Power Estimation (3DPE) models achieve small prediction errors that are bounded by construction. We are the first to perform a novel stress test of our predictive models across a wide range of implementation and design-space parameters Further, we explore model-guided implementation of designs in Ed to achieve minimum power: that is, our models recommend a most-promising set of implementation parameters and constraints, and also provide a priori estimates of 3D power benefits, based on a given design´s post-synthesis and 2D implementation parameters. We achieve ≤10% error in power benefit prediction across various 3DIC designs.