通过解耦结构、功能与时序，提升电路表征学习效率与精度。
DeepSeq2: Enhanced Sequential Circuit Learning with Disentangled Representations
- 将电路分解为结构、功能、时序三类嵌入空间，实现更精细表征
- 采用高效DAG-GNN替代递归传播，执行时间显著降低
- 新监督机制更好捕捉电路状态转换，适合芯片设计场景
电路表征学习在电子设计自动化（EDA）中日益关键，可提升下游任务的模型效率与准确性。尽管DeepSeq已率先实现时序电路学习，但存在执行时间长、架构低效等缺陷。为此，本文提出DeepSeq2框架，通过创新地将电路映射至结构、功能和时序三个独立嵌入空间，更细致地捕捉电路动态的内在复杂性。该框架采用高效的有向无环图神经网络（DAG-GNN），避免DeepSeq中的递归传播，大幅缩短执行时间并增强模型可扩展性。此外，DeepSeq2引入独特监督机制，更有效捕获电路内部的状态转换行为。在功耗估计与可靠性分析任务中，DeepSeq2超越先前方法，树立了新的基准。
原文摘要 · Abstract (English)
Circuit representation learning is increasingly pivotal in Electronic Design Automation (EDA), serving various downstream tasks with enhanced model efficiency and accuracy. One notable work, DeepSeq, has pioneered sequential circuit learning by encoding temporal correlations. However, it suffers from significant limitations including prolonged execution times and architectural inefficiencies. To address these issues, we introduce DeepSeq2, a novel framework that enhances the learning of sequential circuits, by innovatively mapping it into three distinct embedding spaces-structure, function, and sequential behavior-allowing for a more nuanced representation that captures the inherent complexities of circuit dynamics. By employing an efficient Directed Acyclic Graph Neural Network (DAG-GNN) that circumvents the recursive propagation used in DeepSeq, DeepSeq2 significantly reduces execution times and improves model scalability. Moreover, DeepSeq2 incorporates a unique supervision mechanism that captures transitioning behaviors within circuits more effectively. DeepSeq2 sets a new benchmark in sequential circuit representation learning, outperforming prior works in power estimation and reliability analysis.
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