Computer Science > Machine Learning
arXiv:2501.05667 (cs)
[Submitted on 10 Jan 2025 (v1), last revised 26 Mar 2025 (this version, v2)]
Title:TransPlace: Transferable Circuit Global Placement via Graph Neural Network
View a PDF of the paper titled TransPlace: Transferable Circuit Global Placement via Graph Neural Network, by Yunbo Hou and 5 other authors
View PDFHTML (experimental)Abstract:Global placement, a critical step in designing the physical layout of computer chips, is essential to optimize chip performance. Prior global placement methods optimize each circuit design individually from scratch. Their neglect of transferable knowledge limits solution efficiency and chip performance as circuit complexity drastically increases. This study presents TransPlace, a global placement framework that learns to place millions of mixed-size cells in continuous space. TransPlace introduces i) Netlist Graph to efficiently model netlist topology, ii) Cell-flow and relative position encoding to learn SE(2)-invariant representation, iii) a tailored graph neural network architecture for informed parameterization of placement knowledge, and iv) a two-stage strategy for coarse-to-fine placement. Compared to state-of-the-art placement methods, TransPlace-trained on a few high-quality placements-can place unseen circuits with 1.2x speedup while reducing congestion by 30%, timing by 9%, and wirelength by 5%.
| Comments: | Accepted at KDD 2025 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR) |
| Cite as: | arXiv:2501.05667 [cs.LG] |
| (orarXiv:2501.05667v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2501.05667 arXiv-issued DOI via DataCite |
Submission history
From: Yunbo Hou [view email][v1] Fri, 10 Jan 2025 02:33:15 UTC (2,384 KB)
[v2] Wed, 26 Mar 2025 03:19:11 UTC (2,440 KB)
Full-text links:
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
- Other Formats
View a PDF of the paper titled TransPlace: Transferable Circuit Global Placement via Graph Neural Network, by Yunbo Hou and 5 other authors
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
IArxiv Recommender(What is IArxiv?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.