Multiphysics-Informed ML-Assisted Chiplet Floorplanning for Heterogeneous Integration

This paper presents an efficient and accurate method to perform multiphysics-informed floorplan and placement of heterogeneously integrated chiplets. Traditional multiphysics simulations, often impractical in optimization due to high computational cost, are replaced by a high-fidelity and efficient generative model via image-based machine learning (ML). Utilizing the ML model for fast performance assessment, we further accelerate the physical design by developing a dynamic rank-revealing algorithm for solving the underlying large-scale constrained optimization problem. Application to chiplet floorplanning and comparison with prevailing methods have demonstrated the superior performance of the proposed work.