AI 中文总结
针对VLSI设计中传统功耗分析运行时间长的问题，提出LEAP模型，通过线性复杂度图变换器与自监督预训练实现快速准确的逐周期翻转传播预测，集成后可提升布局功耗分析效率与精度
AI 中文摘要
精确的功耗分析对超大规模集成电路（VLSI）设计至关重要，它直接影响功耗优化策略。然而，传统方法常受限于网表中逐周期翻转传播所需的大量运行时间，该过程需将寄存器翻转信息通过组合逻辑传播。为解决此问题，我们提出LEAP，这是首个既能实现高准确率又具备高效率的逐周期翻转传播预测工作。其实现依赖于一种新型线性复杂度图变换器，该变换器可模拟翻转传播，同时结合专门设计的自监督预训练任务，使模型能够捕捉电路的结构与功能。LEAP在翻转传播上比电子设计自动化（EDA）工具实现了7.6倍的加速，且预测结果达到接近完美的精确率-召回率曲线下面积（PR-AUC）0.99。此外，LEAP可与其他基于机器学习的功耗模型无缝集成至LEAP-Power，该集成可直接从综合后网表实现精确的逐周期布局功耗预测，平均绝对百分比误差（MAPE）仅为4.55%。通过绕过网表中的翻转传播，LEAP-Power实现了显著的运行时间增益，比未集成LEAP的模型快5.3倍。
英文摘要
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.
CommentsAccepted by Design Automation Conference (DAC), 2026
