发表机构
McGill University（麦吉尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出STEP-KD方法，利用中间设计阶段作为垫脚石进行序列知识蒸馏，从后期阶段向早期综合后模型转移时序知识，显著降低时序预测误差，提前识别时序问题。
AI 中文摘要
集成电路设计涉及多个设计阶段：逻辑综合、布局规划、布局和布线，每个阶段需要数小时到数周才能完成。在此流程后期发现时序违规会导致代价高昂的迭代返回早期阶段，浪费计算资源并延迟产品发布。虽然从早期阶段数据预测布线后时序可以防止这些失败，但现有的机器学习方法难以应对综合后逻辑描述与布线后物理布局之间的巨大抽象差距。我们提出STEP-KD（通过渐进知识蒸馏的序列时序评估），该方法利用中间设计阶段作为“垫脚石”进行渐进知识转移，而非尝试直接预测。STEP-KD在布线后、布局后和布局规划后阶段训练教师模型，然后通过表示对齐将其知识序列蒸馏到综合后学生模型。在多种电路上的实验表明，与直接蒸馏和监督基线相比，STEP-KD降低了时序预测误差，并且在大多数设置下优于行业标准的静态时序分析（STA）工具。STEP-KD将总负松弛预测的加权平均绝对百分比误差降低至19.78%，而STA为74.84%。我们提出的方法是提前识别时序问题的一步，避免了代价高昂的后期重新设计。
英文摘要
Integrated circuit design involves multiple design stages: logic synthesis, floorplanning, placement, and routing, with each stage taking hours to weeks to complete. Discovering timing violations late in this flow forces costly iterations back to earlier stages, wasting computational resources and delaying product launches. While predicting post-routing timing from early-stage data could prevent these failures, existing machine learning approaches struggle with the massive abstraction gap between post-synthesis logical descriptions and post-routing physical layouts. We propose STEP-KD (Sequential Timing Evaluation via Progressive Knowledge Distillation), which leverages intermediate design stages as ``stepping stones'' for progressive knowledge transfer rather than attempting direct prediction. STEP-KD trains teacher models at the post-routing, post-placement, and post-floorplan stages, then sequentially distills their knowledge to a post-synthesis student model through representation alignment. Experiments on diverse circuits demonstrate that STEP-KD reduces timing prediction error compared to direct distillation and supervised baselines, and in most settings compared to the industry-standard Static Timing Analysis (STA) tool. STEP-KD reduces the weighted mean absolute percentage error of Total Negative Slack prediction to 19.78\%, compared with 74.84\% for STA. Our proposed method is step forward to identify timing problems earlier, avoiding expensive late-stage redesigns.
