Abstract
With minimal compromises on other metrics, eliminating overflow and lowering congestion level of global routing results as much as possible is a crucial topic for reducing violations and hotspots in subsequent design phases. Different from current common practices of using maze routing according to some explicit orders to sequentially re-route particular nets of interest, this paper proposes a collaborative refinement framework that can generate multiple paths simultaneously to enlarge the solution space based on a multi-agent generative model, serving as a flexible post-processing plug-in on existing global routing results to reduce congestion. Experimental results well reveal its effectiveness.
Original language | English |
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Title of host publication | GLSVLSI 2024 - Proceedings of the Great Lakes Symposium on VLSI 2024 |
Editors | Inna Partin-Vaisband, Srinivas Katkoori, Lu Peng, Boris Vaisband, Tooraj Nikoubin |
Publisher | Association for Computing Machinery (ACM) |
Pages | 383-389 |
Number of pages | 7 |
ISBN (Print) | 9798400706059 |
DOIs | |
Publication status | Published - 12 Jun 2024 |
Event | 34th Great Lakes Symposium on VLSI 2024, GLSVLSI 2024 - Clearwater, United States Duration: 12 Jun 2024 → 14 Jun 2024 https://dl.acm.org/doi/proceedings/10.1145/3649476 |
Publication series
Name | Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI |
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Conference
Conference | 34th Great Lakes Symposium on VLSI 2024, GLSVLSI 2024 |
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Country/Territory | United States |
City | Clearwater |
Period | 12/06/24 → 14/06/24 |
Internet address |
Scopus Subject Areas
- General Engineering