Architecture-Aware Multi-Scale Modeling with Self- and Cross-Attention for Dynamic IR Drop Analysis

With the continued advancement of Very Large-Scale Integration (VLSI) technology, IR drop in Power Delivery Networks (PDNs) poses a serious threat to chip performance and reliability. Due to the complex cross-scale interactions between local hotspots and global power paths in real-world scenarios, existing approaches face the challenges of lacking a coupled representation of multi-scale features and ignoring long-range dependencies and spatiotemporal feature fusion. This severely limits the ability of IR drop prediction approaches to capture voltage-drop hotspots, undermining the accuracy and reliability of their predictions. Therefore, this paper proposes a novel prediction framework named IR-Hunter. IR-Hunter first employs heterogeneous convolution kernels to model local electrothermal hotspots and global PDN impedance distributions, precisely capturing hotspot locations and macro-scale current-flow paths critical for chip layout and routing. Then, it adaptively fuses these multi-scale features. Additionally, IR-Hunter learns global power interactions within the PDN to identify long-range voltage coupling effects affecting routing and power-grid stability. Finally, IR-Hunter leverages cross-metal-layer current propagation pathways, efficiently integrating cross-scale spatiotemporal information between the encoder and decoder stages, consistently improving IR drop prediction accuracy during chip design. Comprehensive experiments conducted on 20,578 samples collected from over 20K instances demonstrate IR-Hunter’s superior prediction accuracy, achieving an average 4.57% NRMSE reduction and 4.12% SSIM improvement compared to state-of-the-art approaches. Additionally, to foster advancements in the EDA community, we have open-sourced the code at https://github.com/xhhlzy/IR-Hunter.