Overcoming Training Data Scarcity in Routing Demand Prediction via Ensemble Learning

As CMOS technology scales down, the number of standard cells increases rapidly. The increasing cell count raises the complexity of physical design. Routing is one of the most time-consuming stages in the physical design flow. When routing fails to meet design rules or performance targets, designers must revise earlier stages such as floorplanning or placement. Repeating the routing process causes high design cost and long time-to-market. Early prediction of routing demand helps reduce design iterations. An ensemble learning model based on XGBoost is proposed to predict global routing demand using placement-stage features. The XGBoost-based model achieves higher accuracy than CNN- and FCN-based models, improving R² by 0.12 and 0.125, respectively. The inference speed is also significantly faster, up to 14.95×. Feature importance analysis enables reduction of training and inference overhead with minimal accuracy loss.