DeepSeq2：基于解耦表示的增强型时序电路学习
DeepSeq2: Enhanced Sequential Circuit Learning with Disentangled Representations
AI总结：
针对现有DeepSeq时序电路学习框架执行慢、架构效率低的问题，提出DeepSeq2框架，通过三空间解耦表示、高效DAG-GNN及专属监督机制提升性能，在功耗估算和可靠性分析上超越已有工作。
摘要：
电路表示学习在电子设计自动化（EDA）中愈发关键，可提升模型效率与准确性，为各类下游任务提供支持。DeepSeq 是该领域的代表性成果，它通过编码时序相关性开创了时序电路学习的先河，但存在执行时间长、架构效率低等显著局限。为解决这些问题，我们提出新型框架 DeepSeq2 以增强时序电路学习能力，其创新地将电路映射到结构、功能、时序行为三个不同的嵌入空间，可生成更精细的表示，捕捉电路动态的固有复杂性。DeepSeq2 采用高效的有向无环图神经网络（DAG-GNN），规避了 DeepSeq 使用的递归传播机制，大幅缩短执行时间并提升模型可扩展性。此外，DeepSeq2 融入了独特的监督机制，能更有效地捕捉电路内的状态转换行为。DeepSeq2 树立了时序电路表示学习的新基准，在功耗估算与可靠性分析任务上的表现优于现有方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
