HoliPlace: Holistic Macro Placement via Global Planning and Entropy-Guided Ordering

Macro placement is a critical step in chip design that significantly affects power, performance, and area (PPA). Recent reinforcement learning (RL) methods have shown promising results in this task. However, existing RL-based methods place macros in a fixed order, making each decision myopic and prone to error propagation, as early misplacements irreversibly constrain subsequent placements. To address these limitations, we propose HoliPlace (Holistic Macro Placement via Global Planning and Entropy-Guided Ordering), a Transformer-based RL framework for holistic macro placement. HoliPlace first leverages a Graph AutoEncoder (GAE) to extract netlist and macro features, and then employs an encoder-only Transformer to simultaneously predict position distributions for all macros, enabling global dependencies modeling. To bridge global planning and sequential execution, we introduce an entropy guided ranker that dynamically determines the placement order based on prediction uncertainty. Finally, we adopt Group Relative Policy Optimization (GRPO) to jointly optimize macro positions and their ordering. Experimental results on the ICCAD2015 benchmark demonstrate that HoliPlace improves placement quality across PPA metrics, outperforming existing methods.