Machine-Learning Based Congestion Estimation for Modern FPGAs
Dani Maarouf, Abeer Y. Al-Hyari, Ziad Abuowaimer, Timothy J. Martin, Andrew David Gunter, Gary Gréwal, Shawki M. Areibi, Anthony Vannelli · 2018
Avoiding congestion for routing resources has become one of the most important placement objectives. In this paper, we present a machine-learning model for accurately and efficiently estimating congestion during FPGA placement. Compared with the state-of-the-art machine-learning congestion-estimation model, our results show a 25% improvement in prediction accuracy. This makes our model competitive with congestion estimates produced using a global router. However, our model runs, on average, 291x faster than the global router.