HeLEM-GR: Heterogeneous Global Routing with Linearized Exponential Multiplier Method
Global routing (GR) plays an important role in the VLSI design flow. It not only serves as guidance for the follow-up detailed routing but also provides early design feedback for floorplanning and placement. Global routing engines are desired to provide a high-quality solution within a short time. With the design complexity growing, it becomes increasingly challenging to resolve routing overflow within affordable runtime. For example, ISPD 2024 GPU/ML-enhanced global routing contest has released large-scale industrial cases, which contain up to 50 million cells and 60 million signal nets, causing huge challenges to existing routing algorithms. In this paper, we propose HeLEM-GR, based on the linearized exponential multiplier method and heterogeneous routing kernels to achieve high-quality and ultrafast routing solutions. Our linearized exponential multiplier method can quickly reduce routing overflow. The routing process is extremely fast with GPU-enhanced massive parallelization. Experimental results demonstrate that we can achieve 4.8%-5.8% better quality scores and 1.62×-2.07× speedup compared with the top-3 winners in the ISPD 2024 contest.
Authors
- Yibo Lin (ORCID: https://orcid.org/0000-0002-0977-2774)
- Zaiwen Wen (ORCID: https://orcid.org/0000-0003-1762-0671)
- Chunyuan Zhao
- Zizheng Guo (ORCID: https://orcid.org/0000-0002-0724-5356)
- Rui Wang (ORCID: https://orcid.org/0000-0003-3690-1268)
- Yun Liang (ORCID: https://orcid.org/0000-0002-9076-7998)
Institutions
- Peking University (CN)
- Southwestern University of Finance and Economics (CN)
- Peking University International Hospital (CN)
Publication Details
- Published
- 2024-10-27
- DOI
- https://doi.org/10.1145/3676536.3676650
- Citations
- 5
- Primary Topic
- VLSI and FPGA Design Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.04
Funders
- National Science Foundation
- National Natural Science Foundation of China
- Higher Education Discipline Innovation Project