Learns implicit expert rewards for RL chip placement by inferring trajectories from final layouts, enabling generalization from a single design.
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Pith papers citing it
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cs.AR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
FlowPlace applies flow matching with mask-guided data and hard constraints to chip placement, reporting better PPA metrics, 10-50x faster sampling, and zero overlaps on OpenROAD and ICCAD 2015 benchmarks.
citing papers explorer
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How Can Reinforcement Learning Achieve Expert-level Placement?
Learns implicit expert rewards for RL chip placement by inferring trajectories from final layouts, enabling generalization from a single design.
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FlowPlace: Flow Matching for Chip Placement
FlowPlace applies flow matching with mask-guided data and hard constraints to chip placement, reporting better PPA metrics, 10-50x faster sampling, and zero overlaps on OpenROAD and ICCAD 2015 benchmarks.