RL chip placement learns an implicit reward model from expert trajectories inferred from final layouts, closing the gap to human experts even from a single design.
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cs.AR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
FlowPlace uses flow matching with mask-guided data and hard constraints to generate faster, overlap-free chip placements with better PPA metrics than diffusion models.
citing papers explorer
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How Can Reinforcement Learning Achieve Expert-level Placement?
RL chip placement learns an implicit reward model from expert trajectories inferred from final layouts, closing the gap to human experts even from a single design.
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FlowPlace: Flow Matching for Chip Placement
FlowPlace uses flow matching with mask-guided data and hard constraints to generate faster, overlap-free chip placements with better PPA metrics than diffusion models.