AI RESEARCH
R2G: A Multi-View Circuit Graph Benchmark Suite from RTL to GDSII
arXiv CS.LG
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ArXi:2604.08810v1 Announce Type: cross 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 $10^6$ nodes/edges.