SwiftCTS: 通过少样本校准实现时钟树指标的快速跨设计预测与帕累托优化
SwiftCTS: Fast Cross-Design Prediction and Pareto Optimization of Clock Tree Metrics via Few-Shot Calibration
AI总结：
提出SwiftCTS框架，利用物理信息代理模型和K-shot乘法校准机制，在数秒内训练、亚毫秒推理，实现跨设计时钟树指标的准确预测与帕累托优化。
摘要：
时钟树综合（CTS）是物理设计流程中计算成本高昂的阶段，需要迭代调用EDA工具以探索庞大的配置空间，从而优化功耗、线长和时序偏差。现有的机器学习方法需要昂贵的重新训练或微调周期来适应未见过的宏架构，并且在架构上与穷举组合搜索所需的数百万次评估不匹配。我们提出了SwiftCTS，一个物理信息代理框架，同时解决了这两个局限性。通过将轻量级、基于物理的统计特征与梯度提升集成相结合，SwiftCTS在CPU上训练时间不到五秒，且无需GPU支持即可实现亚毫秒级推理。为了处理分布外（OOD）设计而无需重新训练或微调，我们引入了一种K-shot乘法校准机制，该机制仅需一到两次物理参考运行即可锚定预测，将未见过的宏上的功耗预测误差从24.5%降低到3.3%，线长误差从56.6%降低到1%以下。将该引擎与进化优化器集成，SwiftCTS在十秒内评估了100,000个CTS配置，生成了在OpenROAD流程中经过物理验证的帕累托最优前沿。闭环验证确认了功耗和线长的预测误差低于0.5%，时序偏差预测在OOD基准上在五皮秒以内，在所有目标指标上始终优于默认工具启发式方法。代码公开于：\href{this https URL}{this https URL}
英文摘要：
Clock Tree Synthesis (CTS) is a computationally expensive stage in the physical design flow, requiring iterative EDA tool invocations to navigate a vast configuration space for optimal power, wirelength, and timing skew. Existing machine learning approaches require computationally expensive retraining or fine-tuning cycles to adapt to unseen macro architectures and are architecturally mismatched to the millions of evaluations demanded by exhaustive combinatorial search. We present SwiftCTS, a physics-informed surrogate framework that addresses both limitations simultaneously. By coupling lightweight, physics-grounded statistical features with gradient-boosted ensembles, SwiftCTS trains in under five seconds on a CPU and delivers sub-millisecond inference without GPU support. To handle out-of-distribution (OOD) designs without retraining or fine-tuning, we introduce a K-shot multiplicative calibration mechanism that anchors predictions to just one or two physical reference runs, reducing power prediction error from 24.5\% to 3.3\% and wirelength error from 56.6\% to under 1\% on unseen macros. Integrating this engine with an evolutionary optimizer, SwiftCTS evaluates 100,000 CTS configurations in under ten seconds, yielding Pareto-optimal frontiers that are physically validated within the OpenROAD flow. Closed-loop validation confirms prediction errors below 0.5\% for power and wirelength, and timing skew predictions within five picoseconds on an OOD benchmark, consistently outperforming default tool heuristics across all target metrics. Code publicly available at: \href{https://anonymous.4open.science/r/SwiftCTS-7E6E}{https://github.com/BarsatKhadka/SwiftCTS}
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。
