STEP-KD：面向早期电路时序预测的序列化知识蒸馏

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Abstract: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.

Subjects:	Machine Learning (cs.LG); Hardware Architecture (cs.AR)
Cite as:	arXiv:2610.08457 [cs.LG]
	(or arXiv:2610.08457v1 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2610.08457 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Reza Moravej [view email]
[v1] Mon, 5 Oct 2026 17:33:11 UTC (1,028 KB)