AI RESEARCH
PhysEDA: Physics-Aware Learning Framework for Efficient EDA With Manhattan Distance Decay
arXiv CS.LG
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ArXi:2605.10547v1 Announce Type: new Electronic design automation (EDA) addresses placement, routing, timing analysis, and power-integrity verification for integrated circuits. Learning methods -- attention (Transformer) and reinforcement learning (RL) -- have recently emerged on EDA tasks, yet face two common bottlenecks: vanilla attention's quadratic complexity limits scaling, and data-scarce models overfit statistical noise and amplify weak long-range correlations against the underlying physics.