Global and Local Attention-based Inception U-Net for Static IR Drop Estimation
<|reference_start|>Global and Local Attention-based Inception U-Net for Static IR Drop Estimation: Static IR drop analysis is a fundamental and critical task in chip design since the IR drop will significantly affect the design's functionality, performance, and reliability. However, the process of IR drop analysis can be time-consuming, potentially taking several hours. Furthermore, in the process of fixing violations, it is frequently imperative to do IR drop analysis iteratively, hence exacerbating the computational burden associated with the analysis. Therefore, a fast and accurate IR drop prediction is paramount for reducing the overall time invested in chip design. In this paper, we propose a global and local attention-based Inception U-Net for static IR drop estimation. Our U-Net incorporates components from the Transformer, CBAM, and Inception architectures to enhance its feature capture capability at different scales and improve the accuracy of predicted IR drop. Experimental results demonstrate that our proposed algorithm can achieve the best results among the winning teams of the ICCAD 2023 contest and the state-of-the-art algorithms.<|reference_end|>
@article{chen2024global,
    title={Global and Local Attention-based Inception U-Net for Static IR Drop
  Estimation},
    author={Yilu Chen and Zhijie Cai and Min Wei and Zhifeng Lin and Jianli Chen},
    journal={arXiv preprint arXiv:2406.06541},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06541},
    primaryClass={cs.AR}
}