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CLC number: TP391.7
On-line Access: 2025-10-13
Received: 2024-08-02
Revision Accepted: 2025-03-07
Crosschecked: 2025-10-13
Cited: 0
Clicked: 881
Citations: Bibtex RefMan EndNote GB/T7714
Algorithm and evaluation of generating pseudo-datasets for integrated circuit power analysis
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Zejia LYU, Jizhong SHEN, Xi CHEN. Algorithm and evaluation of generating pseudo-datasets for integrated circuit power analysis[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(9): 1596-1608.
@article{title="Algorithm and evaluation of generating pseudo-datasets for integrated circuit power analysis",
author="Zejia LYU, Jizhong SHEN, Xi CHEN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="26",
number="9",
pages="1596-1608",
year="2025",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400677"
}
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%A Xi CHEN
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400677
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T1 - Algorithm and evaluation of generating pseudo-datasets for integrated circuit power analysis
A1 - Zejia LYU
A1 - Jizhong SHEN
A1 - Xi CHEN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 26
IS - 9
SP - 1596
EP - 1608
%@ 2095-9184
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2400677
Abstract: average power analysis plays a crucial role in the design of large-scale digital integrated circuits (ICs). The integration of data-driven machine learning (ML) methods into the electronic design automation (EDA) fields has increased the demand for extensive datasets. To address this need, we propose a novel pseudo-circuit generation algorithm rooted in graph topology. This algorithm efficiently produces a multitude of power analysis examples by converting randomly generated directed acyclic graphs (DAGs) into gate-level Verilog pseudo-combinational circuit netlists. The subsequent introduction of register units transforms pseudo-combinational netlists into pseudo-sequential circuit netlists. Hyperparameters facilitate the control of circuit topology, while appropriate sequential constraints are applied during synthesis to yield a pseudo-circuit dataset. We evaluate our approach using the mainstream power analysis software, conducting pre-layout average power tests on the generated circuits, comparing their performance against benchmark datasets, and verifying the results through circuit topology complexity analysis and static timing analysis (STA). The results confirm the effectiveness of the dataset, and demonstrate the operational efficiency and robustness of the algorithm, underscoring its research value.
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