Published in ACM Transactions on Design Automation of Electronic Systems (TODAES), 2020
The work done in this paper involves integrating machine learning and deep learning into the FPGA placement step to speed up the process without a decrease in the quality-of-result.
Recommended citation: Hannah Szentimrey, Abeer Al-Hyari, Jeremy Foxcroft, Timothy Martin, David Noel, Gary Grewal, and Shawki Areibi. 2020. Machine Learning for Congestion Management and Routability Prediction within FPGA Placement. ACM Trans. Des. Autom. Electron. Syst. 25, 5, Article 37 (October 2020), 25 pages. DOI:https://doi.org/10.1145/3373269 https://dl.acm.org/doi/10.1145/3373269