Effective free space management for cut-based placement via analytical constraint generation

IP blocks and large macro cells are becoming more prevalent in the physical layout of a design, actually causing an increase in the available free space. We observe that top-down placement based on recursive bisection with multilevel partitioning performs poorly on these porous designs since it lacks a global view of the ideal placement. However, the strength of analytic placement methods lies in their ability to ascertain this global view. Consequently, we propose an enhancement to cut-based placement called analytic constraint generation (ACG). ACG utilizes an analytic engine to distribute available free space appropriately by determining balance constraints for each partitioning step. For one-dimensional placements, our experiments illustrate the large gap between analytic engines, traditional cut-based placement, and ACG as a design becomes increasingly sparse. We also show that for real industry designs, ACG significantly improves the performance of cut-based placement, particularly timing perspective, as implemented within a state-of-the-art industrial placer.