用自监督方法融合电路不同设计阶段信息，提升表示学习效果
DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning
- 借鉴掩码语言建模思想，自监督融合AIG与映射后网表信息
- 在功能ECO和工艺映射任务中性能超越现有开源EDA工具
- 首个专为映射后网表设计的表示学习框架，适合EDA研究者
我们提出DeepCell，一种新型电路表示学习框架，有效整合来自与非门图（AIG）和映射后（PM）网表的多视角信息。其核心采用受掩码语言建模启发的自监督掩码电路建模（MCM）策略，将不同设计阶段的互补电路表示融合为统一且丰富的嵌入向量。据我们所知，DeepCell是首个专门针对PM网表表示学习设计的框架，在预测准确率和重建质量上均树立新基准。通过应用于功能工程变更单（ECO）和工艺映射等关键EDA任务，验证了其实际有效性。大量实验结果表明，DeepCell在效率与性能上显著优于当前最先进的开源EDA工具。
原文摘要 · Abstract (English)
We introduce DeepCell, a novel circuit representation learning framework that effectively integrates multiview information from both And-Inverter Graphs (AIGs) and Post-Mapping (PM) netlists. At its core, DeepCell employs a self-supervised Mask Circuit Modeling (MCM) strategy, inspired by masked language modeling, to fuse complementary circuit representations from different design stages into unified and rich embeddings. To our knowledge, DeepCell is the first framework explicitly designed for PM netlist representation learning, setting new benchmarks in both predictive accuracy and reconstruction quality. We demonstrate the practical efficacy of DeepCell by applying it to critical EDA tasks such as functional Engineering Change Orders (ECO) and technology mapping. Extensive experimental results show that DeepCell significantly surpasses state-of-the-art open-source EDA tools in efficiency and performance.
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