R2G：从RTL到GDSII的多视图电路图基准套件
R2G: A Multi-View Circuit Graph Benchmark Suite from RTL to GDSII
该基准将电路图表示与模型选择解耦，其多视图信息对等设计可迁移到视觉图结构任务（如场景图、点云图）中，用于系统评估表示选择对GNN性能的影响。
原文依据 By decoupling representation choice from model choice, R2G isolates a confound that prior EDA and graph-ML benchmarks leave uncontrolled.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论图神经网络（GNN）越来越多地应用于物理设计任务，如拥塞预测和线长估计，然而不一致的电路表示和缺乏受控评估协议阻碍了进展。我们提出R2G（RTL-to-GDSII），一个多视图电路图基准套件，在30个开源IP核（最多106个节点/边）上标准化了五个阶段感知视图，并保证信息对等（每个视图编码相同的属性集，仅特征附加位置不同）。R2G提供了从DEF到图的端到端流水线，涵盖综合、布局和布线阶段，并附带加载器、统一划分、领域指标和可复现基线。通过将表示选择与模型选择解耦，R2G分离了先前EDA和图机器学习基准未能控制的混淆因素。在使用GINE、GAT和ResGatedGCN的系统研究中，我们发现：（i）视图选择主导模型选择，对于固定GNN，不同表示下测试R2变化超过0.3；（ii）以节点为中心的视图在布局和布线中泛化最佳；（iii）解码器头深度（3-4层）是主要精度驱动因素，将发散的训练转变为近乎完美的预测（R2 > 0.99）。代码和数据集：https://github.com/ShenShan123/R2G。
英文原文摘要 1307 字符
Graph neural networks (GNNs) are increasingly applied to physical design tasks such as congestion prediction and wirelength estimation, yet progress is hindered by inconsistent circuit representations and the absence of controlled evaluation protocols. We present R2G (RTL-to-GDSII), a multi-view circuit-graph benchmark suite that standardizes five stage-aware views with information parity (every view encodes the same attribute set, differing only in where features attach) over 30 open-source IP cores (up to 106 nodes/edges). R2G provides an end-to-end DEF-to-graph pipeline spanning synthesis, placement, and routing stages, together with loaders, unified splits, domain metrics, and reproducible baselines. By decoupling representation choice from model choice, R2G isolates a confound that prior EDA and graph-ML benchmarks leave uncontrolled. In systematic studies with GINE, GAT, and ResGatedGCN, we find: (i) view choice dominates model choice, with Test R2 varying by more than 0.3 across representations for a fixed GNN; (ii) node-centric views generalize best across both placement and routing; and (iii) decoder-head depth (3-4 layers) is the primary accuracy driver, turning divergent training into near-perfect predictions (R2 > 0.99). Code and datasets: https://github.com/ShenShan123/R2G.