FlowPlace: Flow Matching for Chip Placement
On the map: Diffusion models → Flow matching, time series and science
Abstract (arXiv)
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, 10-50× faster sampling efficiency, and zero overlaps.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 10 of its 31 references that are in the map. First posted on arXiv 26 Apr 2026.
Builds on
10 of 31 references.
Closest ancestors · 3
Same direction, newest first
- 2024Gradient-Free Generation for Hard-Constrained Systems2 Dec
- 2023Improving and generalizing flow-based generative models with minibatch optimal transport1 Feb
- 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow7 Sep
Other references · 7
- 2017Attention Is All You Needcited by 59,932
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2021On Joint Learning for Solving Placement and Routing in Chip Designcited by 44
- 2023ChiPFormer: Transferable Chip Placement via Offline Decision Transformercited by 42
- 2023Hier-RTLMP: A Hierarchical Automatic Macro Placer for Large-scale Complex IP Blockscited by 11
- 2024Chip Placement with Diffusion Modelscited by 5
- 2024That Chip Has Sailed: A Critique of Unfounded Skepticism Around AI for Chip Designcited by 5
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Close in wording, no citation either way
- 2024LayoutFlow: Flow Matching for Layout Generation
- 2026Trinity: One Differentiable Physics for Training, Refining and Scoring Generative Floorplanners
- 2026See it to Place it: Evolving Macro Placements with Vision-Language Models
- 2026PlaceReasoner-Beta: Reasoning-Driven Macro Placement and Benchmarking
- 2026LithoGRPO: Fast Inverse Lithography via GRPO Reinforced Flow Matching
- 2025DALI-PD: Diffusion-based Synthetic Layout Heatmap Generation for ML in Physical Design
- 2023LayoutDM: Discrete Diffusion Model for Controllable Layout Generation
- 2024ChipAlign: Instruction Alignment in Large Language Models for Chip Design via Geodesic Interpolation