Late Breaking Results: A Geometric Diffusion Model for Macro Placement Generation

Macro placement is crucial in VLSI design, directly impacting circuit performance. We introduce MacroDiff, a diffusion-based macro placement generative model that captures wirelength relationships instead of directly predicting macro coordinates. By leveraging wirelength as an intermediate representation, MacroDiff naturally preserves circuit connectivity, reduces placement constraints, and enhances solution flexibility while inherently handling rotational and translational invariances. Experiments on ISPD2005 benchmarks show that MacroDiff reduces macro overlap by 91.6%, lowers macro legalization displacement by 74.4%, and improves half-perimeter wirelength (HPWL) by 7.0%. While maintaining the efficiency of generative approaches, MacroDiff generates high-quality placements more reliably, narrowing the gap with state-of-the-art methods. The source code for this work is available at https://github.com/jhy00n/MacroDiff.