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

Researchers present OrderPlace, an AI framework that optimizes macro placement sequencing in chip design by using large language models to discover superior ordering strategies. The work demonstrates that placement order significantly impacts solution quality in physical design, with novel sequences achieving 34% wirelength reduction compared to existing methods.

Chip physical design represents a critical bottleneck in semiconductor manufacturing, where macro placement—positioning large functional blocks on silicon—directly influences power, performance, and area outcomes. Traditionally, placement ordering has relied on static heuristics like area or connectivity metrics, treating sequence determination as a preprocessing step rather than an optimization variable. OrderPlace challenges this assumption by demonstrating that early placement decisions create cascading constraints that fundamentally narrow the solution space, making suboptimal initial choices irreversible.

The framework leverages large language model evolution to explore code-level policies beyond manual heuristics, discovering dynamic physics-inspired mechanisms that outperform conventional approaches. By introducing a lightweight proxy evaluation mechanism using deterministic greedy probing, the researchers efficiently filter candidates without incurring prohibitive computational costs. This architectural choice addresses a practical pain point in chip design optimization where evaluation cycles consume significant engineering resources.

The empirical results on ISPD 2005 benchmarks—34% wirelength reduction versus WireMask-EA and 14% improvement over EGPlace—signal meaningful gains for semiconductor design workflows. Wirelength directly correlates with power consumption, signal integrity, and manufacturing yield, making such improvements valuable across the industry. For semiconductor companies and EDA tool providers, these advances suggest that incorporating learned placement strategies could accelerate design cycles and improve chip performance metrics.

• →OrderPlace uses LLM-guided evolution to automatically discover optimal macro placement ordering strategies, moving beyond static heuristics.
• →Placement sequence order proves decisive in optimization, with early decisions creating irreversible cascading effects on solution quality.
• →A lightweight proxy evaluation mechanism enables efficient candidate filtering without prohibitive computational overhead.
• →The framework achieves 34% wirelength reduction versus WireMask-EA, with direct implications for power consumption and chip yield.
• →Novel physics-inspired dynamic ordering mechanisms discovered by the AI outperform traditional area and connectivity-based approaches.