Physics-Guided Geometric Diffusion for Macro Placement Generation

• URL: http://arxiv.org/abs/2605.16451v1
• Date: Fri, 15 May 2026 04:01:32 GMT
• Title: Physics-Guided Geometric Diffusion for Macro Placement Generation
• Abstract summary: Macro placement is a pivotal stage in VLSI physical design, fundamentally determining the overall chip performance.<n>Recent data-driven placement methods have demonstrated significant potential, yet they often struggle to handle sequential dependencies.<n>We propose MacroDiff+, a physics-guided geometric diffusion framework.<n>On the ISPD2005 MMS benchmarks, MacroDiff+ outperforms state-of-the-art baselines with a 6.1-6.2% reduction in wirelength.
• Score: 5.038274171096632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 https://github.com/jhy00n/MacroDiff-plus.

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