GOALPlace: Begin with the End in Mind
Anthony Agnesina, Rongjian Liang, Geraldo Pradipta, Anand Rajaram, Haoxing Mark Ren · 2025
Co-optimizing placement with congestion is integral to achieving high-quality designs. This paper presents GOALPlace, a learning-based approach to improving placement congestion by controlling cell density. It efficiently learns from an EDA tool's post-route optimized results and uses an empirical Bayes technique to adapt the target to a specific placer's solutions, effectively beginning with the end in mind. Our method enhances correlation with the tool's router and timing-opt engine, while solving placement globally without expensive incremental congestion estimation and mitigation methods. A statistical analysis with hierarchical netlist clustering establishes the importance of density and the potential for an adequate cell density target across placements. Our experiments show that our method, when integrated into an academic GPU-accelerated global placer, consistently produces macro and standard cell placements that match or exceed the quality of commercial tools. Our empirical Bayes methodology also shows a substantial quality improvement over leading academic mixed-size placers, achieving up to 10× fewer design rule check (DRC) violations, a 5% decrease in wirelength, and a 30% and 60% reduction in worst and total negative slack (WNS/TNS).