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DeepCell is a novel circuit representation learning framework that effectively integrates information from multiple perspectives: AIGs and PM netlists. It fuses complementary circuit representations from different design stages into a unified, rich embedding using the Mask Circuit Modeling (MCM) strategy, a self-supervised learning approach inspired by Mask Language Modeling. DeepCell is the first framework explicitly designed for PM netlist representation learning, and sets new standards in both prediction accuracy and reconstruction quality. We demonstrate its practical effectiveness by applying DeepCell to critical EDA tasks such as functional Engineering Change Orders (ECOs) and technology mapping, and extensive experimental results show that it significantly improves efficiency and performance over existing state-of-the-art open-source EDA tools.
Takeaways, Limitations
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Takeaways:
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Presenting the first framework for learning PM netlist representations
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Improved prediction accuracy and reconstruction quality
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Improve efficiency and performance of EDA tasks such as functional ECO and technology mapping.
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Outperforms existing state-of-the-art open source EDA tools
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Limitations:
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The paper does not mention specific Limitations. There may be room for further improvement through future research.
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Further research is needed to determine whether performance improvements for specific EDA tasks can generalize to other tasks.
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More detailed information about the dataset used and experimental setup may be required.
