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Proceedings of the AAAI Conference on Artificial Intelligence

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DynamicRTL: RTL Representation Learning for Dynamic Circu...
Ruiyang Ma, · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Ruiyang Ma Peking University
  • Yunhao Zhou Shanghai Jiao Tong University
  • Yipeng Wang Nanjing University of Aeronautics and Astronautics
  • Yi Liu The Chinese University of Hong Kong
  • Zhengyuan Shi The Chinese University of Hong Kong
  • Ziyang Zheng The Chinese University of Hong Kong
  • Kexin Chen Nanjing University of Aeronautics and Astronautics
  • Zhiqiang He Nanjing University of Aeronautics and Astronautics
  • Lingwei Yan Nanjing University of Aeronautics and Astronautics
  • Gang Chen Nanjing University of Aeronautics and Astronautics
  • Qiang Xu The Chinese University of Hong Kong
  • Guojie Luo Peking University

DOI:

https://doi.org/10.1609/aaai.v40i1.37051

Abstract

There is a growing body of work on using Graph Neural Networks (GNNs) to learn representations of circuits, focusing primarily on their static characteristics. However, these models fail to capture circuit runtime behavior, which is crucial for tasks like circuit verification and optimization. To address this limitation, we introduce DR-GNN (DynamicRTL-GNN), a novel approach that learns RTL circuit representations by incorporating both static structures and multi-cycle execution behaviors. DR-GNN leverages an operator-level Control Data Flow Graph (CDFG) to represent Register Transfer Level (RTL) circuits, enabling the model to capture dynamic dependencies and runtime execution. To train and evaluate DR-GNN, we build the first comprehensive dynamic circuit dataset, comprising over 6,300 Verilog designs and 63,000 simulation traces. Our results demonstrate that DR-GNN outperforms existing models in branch hit prediction and toggle rate prediction. Furthermore, its learned representations transfer effectively to related dynamic circuit tasks, achieving strong performance in power estimation and assertion prediction.

How to Cite

Ma, R., Zhou, Y., Wang, Y., Liu, Y., Shi, Z., Zheng, Z., … Luo, G. (2026). DynamicRTL: RTL Representation Learning for Dynamic Circuit Behavior. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 836–843. https://doi.org/10.1609/aaai.v40i1.37051

Issue

Section

AAAI Technical Track on Application Domains I

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