DUALPlace: Reinforcement Learning based Mixed-size Placement with Multi Modal Cross Attention

As modern circuits scale up, chip placement becomes increasingly challenging. Reinforcement learning (RL) algorithms have shown promise in achieving high-performance placement outcomes. However, existing RL approaches struggled with mixed-size placement challenges primarily due to their inability to model interdependencies between heterogeneous components during co-optimization. This study introduced an RL framework specifically designed for mixed-size placement called DUALPlace. DUALPlace leveraged cross-attention for multi-modal fusion, combining a multi-scale cross-attention vision transformer (Cross-ViT) and a graph attention network (GAT) for different modal feature extraction. DUALPlace incorporated net density maps as part of the global vision feature to enhance congestion analysis, providing a detailed view of layout congestion. Additionally, to reduce diffusion iterations, the netlist graph was partitioned with fixed macro vertices and initially placed together by an RL agent. Compared to the state-of-the-art online RL-based placement method DeepTH, DUALPlace demonstrated significant enhancements of 79.58% and 48.09% in iteration and runtime efficiencies, along with 5.08% and 10.52% improvements in half-perimeter wire length (HPWL) and rectangular uniform wire density (RUDY) metrics on the ISPD2015 benchmarks.