Physics-Guided Geometric Diffusion for Macro Placement Generation
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Computer Science > Machine Learning
Title:Physics-Guided Geometric Diffusion for Macro Placement Generation
Abstract:Macro placement is a pivotal stage in VLSI physical design, fundamentally determining the overall chip performance. Recent data-driven placement methods have demonstrated significant potential, yet they often struggle to handle sequential dependencies and to balance topological connectivity with physical constraints. To bridge this gap, we propose MacroDiff+, a physics-guided geometric diffusion framework. Specifically, we design a dual-domain denoising architecture that couples topological connectivity encoded by heterogeneous GNNs with global geometric context modeled by a Transformer. Furthermore, we introduce Physics-Guided Sampling, an inference strategy that actively steers the generation using explicit gradients to ensure both statistical plausibility and physical validity. On the ISPD2005 MMS benchmarks, MacroDiff+ outperforms state-of-the-art baselines with a 6.1-6.2% reduction in wirelength. Notably, it exhibits superior stability and scalability on large-scale designs where prior methods fail to converge. The source code is available at this https URL.
| Comments: | Accepted to IJCAI 2026. 9 pages, 5 figures |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.16451 [cs.LG] |
| (or arXiv:2605.16451v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.16451
arXiv-issued DOI via DataCite (pending registration)
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