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DAC2025顶会

ATLAS: A Self-Supervised and Cross-Stage Netlist Power Model for Fine-Grained Time-Based Layout Power Analysis

Wenkai Li, Yao Lu, Wenji Fang, Jing Wang, Qijun Zhang, Zhiyao Xie

2025年份
2被引次数

摘要

Accurate power prediction in VLSI design is crucial for effective power optimization, especially as designs get transformed from gate-level netlist to layout stages. However, traditional accurate power simulation requires time-consuming back-end processing and simulation steps, which significantly impede design optimization. To address this, we propose ATLAS, which can predict the ultimate time-based layout power for any new design in the gate-level netlist. To the best of our knowledge, ATLAS is the first work that supports both time-based power simulation and general cross-design power modeling. It achieves such general timebased power modeling by proposing a new pre-training and fine-tuning paradigm customized for circuit power. Targeting golden per-cycle layout power from commercial tools, our ATLAS achieves the mean absolute percentage error (MAPE) of only 0.58%, 0.45%, and 5.12% for the clock tree, register, and combinational power groups, respectively, without any layout information. Overall, the MAPE for the total power of the entire design is <1%, and the inference speed of a workload is significantly faster than the standard flow of commercial tools. PRIMAL [DAC'20] APOLLO [MICRO'21] RTL Yes Yes No No Sengupta et al. [ICCAD'22] SNS [ISCA'22] SNS V2 [MICRO'23] No No Yes MasterRTL [ICCAD'23] Yes Yes PowPredicCT [DAC'24] Layout No ATLAS Netlist Yes Yes Yes Yes * GRANNITE estimates toggle rate instead of power, thus not listed in the table. It is neither time-based nor targeting layout.

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