MAGE: Human-Like Macro Placement via Agentic Multimodal Reasoning
Macro placement still requires substantial manual refinement in industrial physical design flows. We present MAGE (Macro Placement Agentic Engine), a multimodal multi-agent framework for macro placement refinement. MAGE decomposes the macro placement task into a six-phase workflow that combines structured floorplanning rules, visual checks, and iterative refinement. Expert floorplanning knowledge is encoded through natural-language directives and validation criteria, rather than learned from labeled placement data. A tournament-style refinement mode evaluates multiple candidate placements and propagates feedback from higher-quality solutions. We also introduce four metrics for quantifying...
| Authors | Andrew B. Kahng, Sayak Kundu, Bodhisatta Pramanik |
|---|---|
| Primary category | cs.AI |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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