LEAP: A Self-Supervised Per-Cycle Toggle Propagation Model Supports Fast, Transferable, and Early Analysis of Layout Power

2026-08-03

AI Summary

This work addresses the high computational cost of cycle-accurate toggle propagation analysis in traditional VLSI design, which severely limits power optimization efficiency. The authors propose LEAP, the first model to achieve both high accuracy and high efficiency in toggle propagation prediction. LEAP leverages a linear-complexity Graph Transformer architecture combined with self-supervised pretraining to effectively model toggle propagation dynamics in digital circuits, and introduces LEAP-Power—a unified framework that seamlessly integrates into existing power estimation flows. Experimental results demonstrate that LEAP accelerates toggle propagation analysis by 7.6× while achieving a PR-AUC of 0.99, and LEAP-Power attains a mean absolute percentage error (MAPE) of only 4.55%, yielding an overall runtime speedup of 5.3×.

Abstract

Accurate power analysis is critical in VLSI design, as it directly impacts power optimization strategies. However, traditional approaches are often hindered by the substantial runtime required for per-cycle toggle propagation in the netlist, which propagates register toggle information through combinational logic. To address this, we propose LEAP, the first work to enable per-cycle toggle propagation prediction with both high accuracy and efficiency. This is achieved through a novel, linear-complexity graph transformer capable of simulating toggle propagation, along with specially designed self-supervised pre-training tasks that enable the model to capture circuit structure and functionality. LEAP achieves a 7.6x speedup over the EDA tool in toggle propagation, and attains a near-perfect area under the Precision-Recall curve (PR-AUC) of 0.99 for prediction results. Moreover, LEAP can be seamlessly integrated with other machine learning based power models into LEAP-Power. This integration enables precise per-cycle layout power prediction directly from post-synthesis netlists, achieving a mean absolute percentage error(MAPE) of only 4.55%. By bypassing toggle propagation in the netlist, LEAP-Power delivers substantial runtime gains, running 5.3x faster than the model without LEAP.

Problem

• power analysis
• toggle propagation
• VLSI design
• runtime efficiency
• layout power

Innovation

• self-supervised learning
• graph transformer
• toggle propagation
• power analysis
• VLSI design