Climbing the Design Ladder: Sequential Knowledge Distillation for Early-Stage Circuit Timing Prediction

2026-10-05

AI Summary

This study addresses the challenge of inaccurate timing prediction during early-stage integrated circuit design, which incurs substantial rework costs in later stages. To overcome this limitation, we propose STEP-KD, a novel framework introducing the first sequential knowledge distillation mechanism based on intermediate design stages. By leveraging these intermediate stages as stepping stones, the method bridges the abstraction gap inherent in direct prediction. Through representation alignment and a multi-stage teacher-student network architecture, post-routing timing knowledge is progressively transferred to models operating at the synthesis stage. The proposed approach significantly reduces cross-stage timing prediction errors, achieving a weighted mean absolute percentage error of 19.78% for total negative slack. These results demonstrate superior performance compared to conventional static timing analysis tools, highlighting the effectiveness of progressive knowledge transfer for early-stage timing estimation.

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.

Problem

• Integrated Circuit Design
• Timing Prediction
• Knowledge Distillation
• Early-Stage Prediction
• Timing Violations

Innovation

• Sequential Knowledge Distillation
• Circuit Timing Prediction
• Progressive Knowledge Transfer
• Representation Alignment
• Electronic Design Automation