EGGPlacer: Energy-Guided Generative Chip Placement with Diffusion Models

EGGPlacer is an energy‑guided diffusion framework for macro placement that integrates analytical placement feedback and differentiable thermal feedback. By treating these signals as composable energies, the method injects them into reverse diffusion without retraining the generative model. Experiments on public benchmarks show that EGGPlacer achieves competitive mixed‑size HPWL and outperforms other methods in routed wirelength and peak temperature.

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• diffusion
• benchmarks