A Novel Image-Graph Heterogeneous Fusion Framework for Static IR Drop Prediction

IR drop analysis is crucial for ensuring the reliability and performance of integrated circuits (ICs) but poses computational challenges as the IC designs grow larger, especially for ultra deep-submicron VLSI designs. Deep learnings (DL) as the efficiency-promising solutions, mainly employ various CNN-based networks to achieve image-to-image IR drop predictions. However, they neglect and lose the power delivery network (PDN) global spatial features and cell instance topological information. This paper proposes a novel image-graph heterogeneous fusion framework (IGHF), which integrates the effectiveness and complementarity of dual branches (CNN and GNN) for higher prediction performance. In the CNN-based Power ScaleFusion Unet branch, the proposed long-range and local-detail encoder (LLE) integrates seamlessly with the hierarchical and adjacent compensation group (HACG) module. This design facilitates effective multi-scale global-to-local spatial power feature extraction within the PDN and enables adaptive high-to-low-level feature fusion and compensation in the decoder. Moreover, a cell voltage aware (CVA) module in the GNN branch is designed to adaptively aggregate PDN topological features of heterogeneous neighbors of different orders. Comparative experiments demonstrate that the proposed IGHF achieves significant accuracy improvements, outperforming the state-of-the-art MAUNet and widely-used IREDGe methods by considerable margins of 24.6% and 55.0% reduction in prediction error, while the prediction maps possess higher structural fidelity. Transfer experiments indicate that IGHF with transfer learning can improve the accuracy in real circuits with the few-shot real circuit test cases.