Order Matters: Unveiling the Hidden Impact of Macro Placement Sequences via Proxy-Guided LLM Evolution

2026-06-07

AI Summary

This work proposes OrderPlace, a novel framework that treats macro placement order as a learnable optimization dimension, overcoming the limitations of traditional static heuristics which often lead to irreversible suboptimal constraints due to early placement decisions. By integrating large language model–guided evolutionary algorithms, code-level policy generation, and a lightweight surrogate evaluator, OrderPlace efficiently explores dynamic and diverse placement sequencing strategies. Evaluated on the ISPD 2005 benchmark suite, the method reduces wirelength by 34.04% and 14.08% compared to WireMask-EA and EGPlace, respectively, demonstrating the superior effectiveness of the discovered placement policies.

Abstract

Macro placement is a fundamental step in modern chip physical design, playing a crucial role in determining the solution quality of high-dimensional combinatorial optimization problems. Despite recent advancements in machine learning for spatial coordinate determination, the temporal dimension of placement sequencing remains largely governed by static heuristics. In this work, we demonstrate that the placement sequence is not merely a preprocessing step but a decisive factor in optimization, where suboptimal early decisions trigger irreversible domino effects that constrain the solution space. To harness this unexplored dimension, we propose \textbf{OrderPlace}, a proxy-guided LLM evolution framework for automatically discovering macro placement order strategies. Instead of relying on manually crafted heuristics such as area- or connectivity-based ordering, OrderPlace explores a broader space of code-level policies, ranging from static scoring metrics to dynamic physics-inspired mechanisms. To mitigate the prohibitive cost of evaluating sequences, we introduce a lightweight proxy evaluation mechanism that efficiently filters candidates using a deterministic greedy probe. Experimental results on the standard ISPD 2005 benchmarks demonstrate that OrderPlace discovers novel ordering strategies. Compared with WireMask-EA and the state-of-the-art method EGPlace, OrderPlace reduces wirelength by 34.04\% and 14.08\%, respectively.

Problem

• macro placement
• placement sequence
• combinatorial optimization
• chip physical design
• solution space

Innovation

• macro placement sequencing
• LLM-based optimization
• proxy-guided evolution
• chip physical design
• combinatorial optimization