Toward Near-Optimal Floorplanning with Complex Design Constraints for Modern SoCs
Abstract: Modern system-on-chip (SoC) floorplanning is increasingly challenged by highly coupled complex design constraints. Existing methods often relax constraints into objectives via multi-objective optimization, leading to infeasible solutions or degraded quality. To address this, we formulate the problem as a constrained optimization task and propose a novel three-stage analytical framework integrating semidefinite programming (SDP), augmented Lagrangian method (ALM), and second-order cone programming (SOCP), which enforces constraints more rigorously and achieves higher-quality solutions. First, we adopt a two-step SDP formulation to obtain a robust initialization in the non-convex search space. Next, we incorporate differentiable objectives and constraints into an incremental ALM to jointly handle coupled design constraints during global floorplanning. Finally, an SOCP-based legalization strictly enforces feasibility while further reducing area. Experiments on the FloorSet benchmark show that our method satisfies all constraints with the fastest runtime. Compared with state-of-the-art methods, it reduces wirelength and area by 90\% and 16\% on average, and achieves near-optimal results, with wirelength and area being merely 2\% and 1\% larger than the ground-truth references, respectively.