The repository provides congestion estimation visualizations of four kinds
- MEDUSA
- Contains congestion maps generated by MEDUSA framework
- c-DCGAN
- Contains congestion maps generated by c-DCGAN [1]
- groundtruth
- Contains congestion maps generated by NTHU-Route 2.0 [2] or CU-GR [3], respective
- rudy
- Contains congestion maps generated by RUDY [4]
[1] Z. Zhou, Z. Zhu, J. Chen, Y. Ma, B. Yu, T. Ho, G. Lemieux, and A. Ivanov. 2019. Congestion-aware Global Routing using Deep Convolutional Generative Adversarial Networks. In 2019 ACM/IEEE 1st Workshop on Machine Learning for CAD (MLCAD)
[2] Y. Chang, Y. Lee, and T. Wang. 2008. NTHU-Route 2.0: A fast and stable global router. In IEEE/ACM International Conference on Computer-Aided Design.
[3] Jinwei Liu, Chak-Wa Pui, Fangzhou Wang, and Evangeline F. Y. Young. 2020. CUGR: Detailed-Routability-Driven 3D Global Routing with Probabilistic Resource Model. In 2020 57th ACM/IEEE Design Automation Conference (DAC).
[4] P. Spindler and F. M. Johannes. 2007. Fast and Accurate Routing Demand Estimation for Efficient Routability-driven Placement. In Design, Automation Test in Europe.