FlowPlace: Flow Matching for Chip Placement
Chip placement plays an important role in physical design. While generative models like diffusion models offer promising learning-based solutions, current methods have the following limitations: they use random synthetic data for pre-training, require long sampling times, and often result in overlaps due to their dependence on gradient-based solvers during the sampling process. To overcome these issues, we propose FlowPlace, which features mask-guided synthetic data generation, flow-based efficient training with flexible prior injection, and hard constraint sampling for overlap-free layouts. Experiments on OpenROAD and ICCAD 2015 benchmarks show FlowPlace achieves better PPA metrics, 10-5...
| Authors | Peng Xie, Ke Xue, Yunqi Shi et al. |
|---|---|
| Primary category | cs.AR |
| Published | 26 April 2026 |
| Last updated at source | 1 June 2026 |
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