Recent approaches to congestion prediction and routability-driven placement have made progress, yet important limitations remain. Most existing models rely on heuristic-based intermediate features (e.g., RUDY, pin density) rather than fully end-to-end learning, and many capture either netlist topology or geometric placement context, but not both, limiting their performance. Moreover, when model gradients are used to guide placers, they are often propagated through such intermediate features, reducing their accuracy and effectiveness. To address these gaps, we propose TopoGeoNet , a heterogeneous neural network that unifies graph-based message passing with convolutional U-Net processing over spatial grids. Through bi-directional node–grid communication, TopoGeoNet jointly leverages netlist topology and geometric placement information for accurate congestion prediction. Trained on ISPD’2016 FPGA benchmarks, TopoGeoNet achieves an 8.3% improvement in prediction accuracy over state-of-the-art ML baselines. When integrated into a routability-driven FPGA placer, it improves congestion scores by 16% on average and reduces total place-and-route runtime by 5% without degrading routed wirelength. Compared with state-of-the-art ML prediction models that provide inference and gradients through intermediate features, TopoGeoNet delivers superior routability results, highlighting the advantages of fully end-to-end prediction and differentiability.
Xiong et al. (2026) studied this question.