MacroAgent: Regularity-Aware Macro Legalization with LLM-Agent-Designed Contour Algorithms
MacroAgent is a novel four-stage macro legalization framework for VLSI designs that leverages LLMs to discover heuristic regularity-aware contour algorithms The approach achieves 2-8 fold improvement in layout regularity and 3-5% reduction in routed wirelength compared to state-of-the-art methods on TILOS and Chipyard benchmarks End-to-end evaluation via Cadence Innovus confirms tangible PPA gains: 2.9% lower routed wirelength and 68.3% TNS improvement over DREAMPlace baseline The framework addr
Analysis
TL;DR
- MacroAgent is a novel four-stage macro legalization framework for VLSI designs that leverages LLMs to discover heuristic regularity-aware contour algorithms
- The approach achieves 2-8 fold improvement in layout regularity and 3-5% reduction in routed wirelength compared to state-of-the-art methods on TILOS and Chipyard benchmarks
- End-to-end evaluation via Cadence Innovus confirms tangible PPA gains: 2.9% lower routed wirelength and 68.3% TNS improvement over DREAMPlace baseline
- The framework addresses key limitations of existing approaches—lack of robustness, high computational cost, and neglect of inter-macro regularity
- LLM-agent-designed contour algorithms enable robust, effective heuristic solutions with acceptable runtime performance
Why It Matters
This work represents a significant intersection of large language models and electronic design automation (EDA), demonstrating that LLMs can autonomously discover effective algorithmic heuristics for a critical physical design problem. For AI practitioners and EDA researchers, it showcases a novel paradigm where LLMs act as algorithm designers rather than mere code assistants, with direct impact on chip performance, power, and area (PPA) metrics that matter to the semiconductor industry.
Technical Details
- Four-stage pipeline: The framework consists of clustering, contour generation, template matching, and inter-cluster refinement, addressing macro legalization as a structured optimization problem
- LLM-driven algorithm discovery: Large Language Models are employed to autonomously generate multiple effective heuristic regularity-aware contour algorithms, rather than relying on hand-crafted heuristics
- Regularity-aware design: Unlike prior work, MacroAgent explicitly considers regularity between macros, which is critical for manufacturability and downstream routing quality
- Benchmark validation: Evaluated on TILOS and Chipyard benchmarks, showing 2-8x layout regularity improvement, 3-5% routed wirelength reduction, and comparable congestion after global routing
- Industry-grade evaluation: End-to-end testing through Cadence Innovus place-and-route flow confirmed 2.9% lower routed wirelength and 68.3% total negative slack (TNS) improvement over DREAMPlace, with 1.8% wirelength reduction when integrated into the Innovus macro placement flow
Industry Insight
- The successful application of LLMs as autonomous algorithm designers in EDA signals a broader trend: domain-specific heuristic discovery through generative AI will become a competitive differentiator in chip design toolchains
- The tangible PPA improvements validated through industry-standard Cadence Innovus flows demonstrate that AI-driven EDA solutions can move beyond academic benchmarks to real-world deployment, accelerating adoption timelines
- Foundries and fabless companies should monitor this approach as a potential replacement or augmentation to existing legalization flows, particularly for large-scale designs where macro regularity directly impacts yield and performance
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