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A Self-adaptive Weighted-average Wirelength Model for Global Placement
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    Abstract:

    The existing EDA tools address the global placement problem of very large scale integration (VLSI) physical design by minimizing the sum of half-perimeter wirelength (HPWL) under density constraints. However, the non-differentiability of HPWL renders gradient-based advanced optimization methods inapplicable directly to global placement. Consequently, the weighted-average wirelength (WAWL) model is often employed to approximate HPWL, but it struggles to achieve a balance between smoothness and accuracy. This paper introduces an improved self-adaptive weighted-average wirelength (SaWAWL) model. It dynamically adjusts the weighted factors γ for each wire’s actual length, ensuring both smoothness and reduced error in fitting HPWL. The proposed model enhances the quality of global placement. A global placer based on this model is implemented and validated on the DAC 2012 open benchmark. The results indicate a 3.69% reduction in the total sum of half-perimeter wirelength.

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  • Online: April 28,2025