A technical paper titled “Reinforcement Learning Guided Detailed Routing for Custom Circuits” was published by researchers at UT Austin, Princeton University, and NVIDIA.
“This paper presents a novel detailed routing framework for custom circuits that leverages deep reinforcement learning to optimize routing patterns while considering custom routing constraints and industrial design rules. Comprehensive post-layout analyses based on industrial designs demonstrate the effectiveness of our framework in dealing with the specified constraints and producing sign-off-quality routing solutions, ” states the paper.
Find the technical paper here. Published March 2023.
Hao Chen, Kai-Chieh Hsu, Walker J. Turner, Po-Hsuan Wei, Keren Zhu, David Z. Pan, and Haoxing Ren. 2023. Reinforcement Learning Guided Detailed Routing for Custom Circuits. In Proceedings of the 2023 International Symposium on Physical Design (ISPD ’23). Association for Computing Machinery, New York, NY, USA, 26–34. https://doi.org/10.1145/3569052.3571874.
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