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
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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它引用的顶会 Paper8
- NodeFormer: A Scalable Graph Structure Learning Transformer for Node ClassificationQitian Wu, Wentao Zhao, Zenan Li, David P. Wipf 等NeurIPS 2022 · 被引用 472 次
- DeepGate: learning neural representations of logic gatesMin Li, Sadaf Khan, Zhengyuan Shi, Naixing Wang 等DAC 2022 · 被引用 55 次
- APOLLO: An Automated Power Modeling Framework for Runtime Power Introspection in High-Volume Commercial MicroprocessorsZhiyao Xie, Xiaoqing Xu, Matt Walker, Joshua Knebel 等MICRO 2021 · 被引用 55 次
- Functionality matters in netlist representation learningZiyi Wang, Chen Bai, Zhuolun He, Guangliang Zhang 等DAC 2022 · 被引用 45 次
- DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained DiffusionQitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He 等ICLR 2023 · 被引用 26 次
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