Close menu


Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

Efficient FPGA Routing using Reinforcement Learning

Farooq, Umer, Hasan, Najam Ul, Baig, Imran and Zghaibeh, Manaf (2021) Efficient FPGA Routing using Reinforcement Learning. In: 2021 12th International Conference on Information and Communication Systems (ICICS).

Item Type: Conference or Workshop Item (Paper)


With every new generation, Field Pro-grammable Gate Arrays (FPGAs) are getting more complex and so are their back end flow. Routing is an important step of FPGA back end flow that takes a lot of time. Making it more efficient in terms of execution time without the loss of quality is a huge challenge. In this work, we propose to use Reinforcement Learning(RL) based routing technique to make the FPGA routing faster. We use a comprehensive set of homogeneous and heterogeneous benchmarks to compare the RL-based technique with the conventional negotiated congestion driven routing technique. Experimental results reveal that for quick turn around, when compared to negotiated congestion technique, the RL-based technique gives, on average, 35% more accurate results about the final design. Moreover, for the complete routing step, the RL-based technique gives 30% speed up while giving similar quality of results.

Full text not available from this repository.

More Information

Depositing User: Umer Farooq


Item ID: 14972
Official URL:

Users with ORCIDS

ORCID for Umer Farooq: ORCID iD

Catalogue record

Date Deposited: 04 Aug 2022 11:00
Last Modified: 04 Aug 2022 11:00


Author: Umer Farooq ORCID iD
Author: Najam Ul Hasan
Author: Imran Baig
Author: Manaf Zghaibeh


Engineering > Electrical Engineering

Actions (login required)

View Item (Repository Staff Only) View Item (Repository Staff Only)