{"slug": "macroagent-regularity-aware-macro-legalization-with-llm-agent-designed-contour", "title": "MacroAgent: Regularity-Aware Macro Legalization with LLM-Agent-Designed Contour Algorithms", "summary": "Researchers introduced MacroAgent, a four-stage framework that uses large language models to design contour algorithms for macro legalization in VLSI designs, achieving a 2 to 8 fold improvement in layout regularity and a 3% to 5% reduction in routed wirelength on TILOS and Chipyard benchmarks compared to state-of-the-art methods. End-to-end evaluation with Cadence Innovus showed 2.9% lower routed wirelength and 68.3% TNS improvement over the DREAMPlace baseline.", "body_md": "arXiv:2608.24946v1 Announce Type: new\nAbstract: Macros constitute a large part of the core area in modern very large-scale integration (VLSI) designs. Moreover, macro positions have a significant impact on the final quality of result (QoR), and macro legalization is typically the final step in determining the macro positions. However, existing approaches related to macro legalization either lack robustness or incur substantial computational costs or neglect the regularity between macros. To address these limitations, we introduce MacroAgent. The novel framework is a four-stage approach: clustering, contour generation, template matching, and inter-cluster refinement. We propose leveraging Large Language Models (LLMs) to discover multiple, effective heuristic regularity-aware contour algorithms. This framework successfully generates robust and effective algorithmic solutions for macro legalization. Compared with state-of-the-art macro legalization works, experimental results on TILOS and Chipyard benchmarks demonstrate a 2 to 8 fold improvement in layout regularity, a 3% to 5% reduction in routed wirelength with comparable congestion after global routing, and significantly better robustness with an acceptable runtime. Furthermore, end-to-end evaluation through Cadence Innovus place-and-route confirms that the regularity improvements translate into tangible PPA gains, including 2.9% lower routed wirelength and 68.3% TNS improvement over the DREAMPlace macro legalization baseline; it also achieves 1.8% lower routed wirelength when integrated into the Innovus macro placement flow.", "url": "https://wpnews.pro/news/macroagent-regularity-aware-macro-legalization-with-llm-agent-designed-contour", "canonical_source": "https://arxiv.org/abs/2608.24946", "published_at": "2026-08-27 04:00:00+00:00", "updated_at": "2026-08-27 04:18:59.343442+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-research"], "entities": ["MacroAgent", "TILOS", "Chipyard", "Cadence Innovus", "DREAMPlace"], "alternates": {"html": "https://wpnews.pro/news/macroagent-regularity-aware-macro-legalization-with-llm-agent-designed-contour", "markdown": "https://wpnews.pro/news/macroagent-regularity-aware-macro-legalization-with-llm-agent-designed-contour.md", "text": "https://wpnews.pro/news/macroagent-regularity-aware-macro-legalization-with-llm-agent-designed-contour.txt", "jsonld": "https://wpnews.pro/news/macroagent-regularity-aware-macro-legalization-with-llm-agent-designed-contour.jsonld"}}