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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.07996 |
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| _version_ | 1866916786552700928 |
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| author | Huang, Junlang Chen, Hao Luo, Li Cai, Yong Zhang, Lexin Ma, Tianhao Zhang, Yitian Guan, Zhong |
| author_facet | Huang, Junlang Chen, Hao Luo, Li Cai, Yong Zhang, Lexin Ma, Tianhao Zhang, Yitian Guan, Zhong |
| contents | This paper presents a hybrid model combining Transformer and CNN for predicting the current waveform in signal lines. Unlike traditional approaches such as current source models, driver linear representations, waveform functional fitting, or equivalent load capacitance methods, our model does not rely on fixed simplified models of standard-cell drivers or RC loads. Instead, it replaces the complex Newton iteration process used in traditional SPICE simulations, leveraging the powerful sequence modeling capabilities of the Transformer framework to directly predict current responses without iterative solving steps. The hybrid architecture effectively integrates the global feature-capturing ability of Transformers with the local feature extraction advantages of CNNs, significantly improving the accuracy of current waveform predictions. Experimental results demonstrate that, compared to traditional SPICE simulations, the proposed algorithm achieves an error of only 0.0098. These results highlight the algorithm's superior capabilities in predicting signal line current waveforms, timing analysis, and power evaluation, making it suitable for a wide range of technology nodes, from 40nm to 3nm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07996 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fusing Global and Local: Transformer-CNN Synergy for Next-Gen Current Estimation Huang, Junlang Chen, Hao Luo, Li Cai, Yong Zhang, Lexin Ma, Tianhao Zhang, Yitian Guan, Zhong Signal Processing Machine Learning This paper presents a hybrid model combining Transformer and CNN for predicting the current waveform in signal lines. Unlike traditional approaches such as current source models, driver linear representations, waveform functional fitting, or equivalent load capacitance methods, our model does not rely on fixed simplified models of standard-cell drivers or RC loads. Instead, it replaces the complex Newton iteration process used in traditional SPICE simulations, leveraging the powerful sequence modeling capabilities of the Transformer framework to directly predict current responses without iterative solving steps. The hybrid architecture effectively integrates the global feature-capturing ability of Transformers with the local feature extraction advantages of CNNs, significantly improving the accuracy of current waveform predictions. Experimental results demonstrate that, compared to traditional SPICE simulations, the proposed algorithm achieves an error of only 0.0098. These results highlight the algorithm's superior capabilities in predicting signal line current waveforms, timing analysis, and power evaluation, making it suitable for a wide range of technology nodes, from 40nm to 3nm. |
| title | Fusing Global and Local: Transformer-CNN Synergy for Next-Gen Current Estimation |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2504.07996 |