New benchmark evaluates GNN graph coarsening for chip design

Researchers have introduced CTS-Bench, a new benchmark suite designed to evaluate the trade-offs between graph coarsening techniques and the accuracy of Graph Neural Networks (GNNs) for Clock Tree Synthesis (CTS) in electronic design automation. The benchmark includes 4,860 converged physical design solutions, allowing for systematic analysis of how graph coarsening impacts prediction accuracy and computational efficiency. While coarsening can significantly reduce memory usage and training time, the study found it often removes critical structural information, leading to poor performance on CTS-specific tasks like clock skew prediction. AI

IMPACT Provides a standardized method to assess GNN performance for chip design tasks, potentially guiding future development of more efficient AI models in EDA.

RANK_REASON This is a research paper introducing a new benchmark suite for evaluating specific techniques within AI for a specialized domain. [lever_c_demoted from research: ic=1 ai=1.0]

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• Barsat Khadka
• CTS-Bench
• Graph Neural Networks

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