MacroSPA: Spatial Prior Adaptation for Diffusion-based Macro Placement

Macro placement determines the spatial organization of large circuit blocks and affects subsequent standard-cell placement and routing. However, the spatial preferences inherited by pretrained placers may not align with target design objectives. We propose **MacroSPA**, a post-training framework for **S**patial **P**rior **A**daptation (SPA) that enables the same pretrained diffusion-based macro placer to acquire distinct spatial priors directly from target context-placement samples. These samples can encode prescribed preferences through synthetic generation or capture layout patterns produced by an existing placement optimizer. SPA adjusts how features of the current layout enter graph message passing and feature fusion while updating only 3.03% of the adapted model's parameters and keeping the pretrained backbone frozen. The framework complements SPA with Minkowski Guided Refinement to reduce residual geometric conflicts and with size-adaptive constraint evaluation for efficient inference on large circuits. Experiments show that MacroSPA acquires distinct spatial priors and can improve downstream outcomes relative to the matched pipeline without SPA. It also achieves competitive routing and post-route performance among the evaluated methods. The relative benefits of different spatial priors vary across circuits and objectives, highlighting the practical value of adapting the inherited spatial prior to the target design.