MAGE: Human-Like Macro Placement via Agentic Multimodal Reasoning
MAGE is a multimodal, multi-agent framework for macro placement refinement in chip design, combining structured floorplanning rules, visual checks, and iterative refinement. In tests across nine designs, MAGE achieved geometric-mean improvements of 11.1%-19.3% in Worst Negative Slack (WNS) and 70.0%-74.0% in Total Negative Slack (TNS) over commercial macro placers. On three designs with human-expert baselines, MAGE outperformed human experts by 18.3% in WNS and 72.5% in TNS, and also improved human-likeness metrics by 6%-48% over all baselines.
Why it matters: This work demonstrates a significant advance in automated chip floorplanning, showing that a multimodal, agentic approach can outperform both commercial tools and human experts in macro placement.
Full story at: arXiv AI/ML ↗