Papers › PARSAC: Fast, Human-quality Floorplanning for Modern SoCs with Complex Design Constraints
PARSAC: Fast, Human-quality Floorplanning for Modern SoCs with Complex Design Constraints
Hesham Mostafa, Uday Mallappa, Mikhail Galkin, Mariano Phielipp, Somdeb Majumdar
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The floorplanning of Systems-on-a-Chip (SoCs) and of chip sub-systems is a crucial step in the physical design flow as it determines the optimal shapes and locations of the blocks that make up the system. Simulated Annealing (SA) has been the method of choice for tackling classical floorplanning problems where the objective is to minimize wire-length and the total placement area. The goal in industry-relevant floorplanning problems, however, is not only to minimize area and wire-length, but to do that while respecting hard placement constraints that specify the general area and/or the specific locations for the placement of some blocks. We show that simply incorporating these constraints into the SA objective function leads to sub-optimal, and often illegal, solutions. We propose the Constraints-Aware Simulated Annealing (CA-SA) method and show that it strongly outperforms vanilla SA in floorplanning problems with hard placement constraints. We developed a new floorplanning tool on top of CA-SA: PARSAC (Parallel Simulated Annealing with Constraints). PARSAC is an efficient, easy-to-use, and massively parallel floorplanner. Unlike current SA-based or learning-based floorplanning tools that cannot effectively incorporate hard placement-constraints, PARSAC can quickly construct the Pareto-optimal legal solutions front for constrained floorplanning problems. PARSAC also outperforms traditional SA on legacy floorplanning benchmarks. PARSAC is available as an open-source repository for researchers to replicate and build on our result.
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