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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2402.10937 |
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| _version_ | 1866909110780297216 |
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| author | Li, Hailiang Huo, Yan Wang, Yan Yang, Xu Hao, Miaohui Wang, Xiao |
| author_facet | Li, Hailiang Huo, Yan Wang, Yan Yang, Xu Hao, Miaohui Wang, Xiao |
| contents | As the modern CPU, GPU, and NPU chip design complexity and transistor counts keep increasing, and with the relentless shrinking of semiconductor technology nodes to nearly 1 nanometer, the placement and routing have gradually become the two most pivotal processes in modern very-large-scale-integrated (VLSI) circuit back-end design. How to evaluate routability efficiently and accurately in advance (at the placement and global routing stages) has grown into a crucial research area in the field of artificial intelligence (AI) assisted electronic design automation (EDA). In this paper, we propose a novel U-Net variant model boosted by an Inception embedded module to predict Routing Congestion (RC) and Design Rule Checking (DRC) hotspots. Experimental results on the recently published CircuitNet dataset benchmark show that our proposed method achieves up to 5% (RC) and 20% (DRC) rate reduction in terms of Avg-NRMSE (Average Normalized Root Mean Square Error) compared to the classic architecture. Furthermore, our approach consistently outperforms the prior model on the SSIM (Structural Similarity Index Measure) metric. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10937 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Lightweight Inception Boosted U-Net Neural Network for Routability Prediction Li, Hailiang Huo, Yan Wang, Yan Yang, Xu Hao, Miaohui Wang, Xiao Hardware Architecture Artificial Intelligence Computational Engineering, Finance, and Science Computer Science and Game Theory Machine Learning As the modern CPU, GPU, and NPU chip design complexity and transistor counts keep increasing, and with the relentless shrinking of semiconductor technology nodes to nearly 1 nanometer, the placement and routing have gradually become the two most pivotal processes in modern very-large-scale-integrated (VLSI) circuit back-end design. How to evaluate routability efficiently and accurately in advance (at the placement and global routing stages) has grown into a crucial research area in the field of artificial intelligence (AI) assisted electronic design automation (EDA). In this paper, we propose a novel U-Net variant model boosted by an Inception embedded module to predict Routing Congestion (RC) and Design Rule Checking (DRC) hotspots. Experimental results on the recently published CircuitNet dataset benchmark show that our proposed method achieves up to 5% (RC) and 20% (DRC) rate reduction in terms of Avg-NRMSE (Average Normalized Root Mean Square Error) compared to the classic architecture. Furthermore, our approach consistently outperforms the prior model on the SSIM (Structural Similarity Index Measure) metric. |
| title | A Lightweight Inception Boosted U-Net Neural Network for Routability Prediction |
| topic | Hardware Architecture Artificial Intelligence Computational Engineering, Finance, and Science Computer Science and Game Theory Machine Learning |
| url | https://arxiv.org/abs/2402.10937 |