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

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 i...

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Bibliographic Details
Published in:2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7
Main Authors: Li, Wenkai, Lu, Yao, Fang, Wenji, Wang, Jing, Zhang, Qijun, Xie, Zhiyao
Format: Conference Proceeding
Language:English
Published: IEEE 22.06.2025
Subjects:
Accuracy
Estimation
Integrated circuit modeling
Layout
Logic gates
Optimization
Predictive models
Registers
Very large scale integration
Online Access:Get full text
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Summary: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. 5 8 \%, ~} {0. 4 5 \%}, and {5. 1 2 \%} 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 \lt1 \%, and the inference speed of a workload is significantly faster than the standard flow of commercial tools.
DOI:10.1109/DAC63849.2025.11132751