Order Matters: Unveiling the Hidden Impact of Macro Placement Sequences via Proxy-Guided LLM Evolution
arXiv:2606.08904v1 Announce Type: new 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 d
The increasing complexity of chip design and the limitations of static heuristics are driving research into more dynamic and intelligent optimization methods for physical design.
This research highlights a critical, previously underestimated factor in chip design optimization, potentially leading to significant improvements in chip performance, power efficiency, and manufacturing yield.
The understanding of macro placement sequencing shifts from a static preprocessing step to a dynamic, 'decisive factor' in high-dimensional combinatorial optimization problems for chip physical design.
- · Chip design tool vendors
- · Semiconductor manufacturers
- · AI/ML researchers in EDA
- · AI hardware developers
- · Companies reliant on static chip design heuristics
- · Legacy EDA tool providers
Improved chip performance and efficiency due to better macro placement sequences enabled by AI-guided evolution.
Faster innovation cycles in semiconductor manufacturing as optimization becomes less heuristic-dependent and more intelligent.
Enhanced global compute capacity and capability due to more efficiently designed leading-edge silicon, impacting AI and other compute-intensive fields.
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Read at arXiv cs.AI