Problem
Research questions and friction points this paper is trying to address.
Automated macro placement
VLSI physical design
proxy objectives
visual layout context
downstream physical-design feedback
PlaceReasoner-Beta: Reasoning-Driven Macro Placement and Benchmarking
🤖 AI Summary
针对VLSI物理设计中的自动宏放置挑战，提出PlaceReasoner-Beta框架，通过视觉-语言模型、几何验证器和物理验证器等组件实现闭环推理优化。
📝 Abstract
Automated macro placement remains a fundamental challenge in VLSI physical design. Despite decades of research, existing approaches predominantly optimize hand-crafted proxy objectives, such as estimated wirelength, and typically produce placements through one-shot numerical optimization, limiting their ability to incorporate visual layout context, codified design expertise, and downstream physical-design feedback in a unified loop. We present PlaceReasoner-Beta, a verifier-guided multi-agent framework that reformulates macro placement as a closed-loop reasoning problem rather than black-box optimization. A vision-language model (VLM) planner generates candidate placements from the floorplan image, macro specifications, and connectivity structure; a geometric verifier enforces physical legality and expert placement principles; a physical verifier refines candidates using early implementation feedback; and a post-route optimizer further improves promising layouts using final PPA. To enable reproducible evaluation, we introduce PlaceReasoner-Bench, a fully open end-to-end benchmark built from open RTL designs, EDA tools, and technology libraries. It comprises 8 designs at two aspect ratios, yielding 16 tasks with fixed floorplans and I/O assignments, so methods differ only in macro positions and orientations and are evaluated using routed PPA and DRC rather than pre-route proxies. Across the benchmark, PlaceReasoner-Beta achieves the best timing among DRC-clean methods on all square tasks, reducing post-route TNS by 61.2% at 1:1 and 53.0% at 2:1 relative to the classical baseline field. It also shortens routed wirelength on most designs despite never explicitly optimizing it, demonstrating that reasoning over spatial structure under physical-design feedback can improve end-to-end layout quality beyond proxy-objective optimization.
Innovation
Methods, ideas, or system contributions that make the work stand out.
verifier-guided multi-agent framework
vision-language model (VLM) planner
closed-loop reasoning
PlaceReasoner-Bench
end-to-end benchmark
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