Presentation
Late Breaking Results: A Coarse-to-Fine PCB Placement Framework via LLM-Guided Component Grouping and Group-Aware Placement
DescriptionThis paper introduces a coarse-to-fine printed circuit board (PCB) placement framework bridging high-level functional design intent and physical implementation. First, a large language model (LLM)-guided component grouping engine utilizes multi-modal feature serialization, topology-enriched retrieval-augmented generation (RAG), and multi-agent reasoning to capture functional constraints missed by standard netlists. Subsequently, a group-aware placement phase employs a hierarchical initial placement strategy via B*-tree packing to physically realize these modules. This prevents the pitfalls of blind initialization, providing an optimal starting point for group-aware global placement and constraint graph-based mixed integer linear programming (MILP) legalization. Experimental results demonstrate that the proposed framework significantly surpasses advanced placers in post-routing quality.
Event Type
Late Breaking Results
TimeMonday, July 275:29pm - 5:32pm PDT
LocationExhibit Hall