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アイテム
Graph Neural Network Output for Dataset Duplication Detection on Analog Integrated Circuit Recognition System
http://hdl.handle.net/10458/0002001716
http://hdl.handle.net/10458/00020017169e40fbcd-cbbf-42fd-8316-1449e4a89251
| 名前 / ファイル | ライセンス | アクション |
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| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||||||
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| 公開日 | 2025-08-27 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Graph Neural Network Output for Dataset Duplication Detection on Analog Integrated Circuit Recognition System | |||||||||||||
| 言語 | en | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| キーワード | analog circuit design | |||||||||||||
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| 言語 | en | |||||||||||||
| キーワード | artificial intelligence | |||||||||||||
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| 言語 | en | |||||||||||||
| キーワード | Big data | |||||||||||||
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| 言語 | en | |||||||||||||
| キーワード | graph neural network | |||||||||||||
| 資源タイプ | ||||||||||||||
| 資源タイプ | journal article | |||||||||||||
| アクセス権 | ||||||||||||||
| アクセス権 | open access | |||||||||||||
| 著者 |
Mannan, Arif Abdul
× Mannan, Arif Abdul
× 淡野, 公一
WEKO
7152
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| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | In the need for artificial intelligence application on the analog circuit design automation, larger and larger datasets containing analog and digital circuit pieces are required to support the analog circuit recognition systems. Since analog circuits with almost similar designs could produce completely different outputs, in case of poor netlist to graph abstraction, larger netlist input circuits could generate larger graph dataset duplications, leading to poor performance of the circuit recognition. In this study, a technique to detect graph dataset duplication on big data applications is introduced by utilizing the output vector representation (OVR) of the untrained Graph Neural Network (GNN). By calculating the multi-dimensional OVR output data into 2-dimentional (2D) representation, even the random weighted untrained GNN outputs are observed to be capable of distinguishing between each graph data inputs, generating different output for different graph input while providing identical output for the same duplicated graph data, and allowing the dataset’s duplication detection. The 2D representation is also capable of visualizing the overall datasets, giving a simple overview of the relation of the data within the same and different classes. From the simulation result, despite being affected by the floating-point calculation accuracy and consistency deficiency, the F1 score using floating-point identical comparisons are observed with an average of 96.92% and 93.70% when using CPU and GPU calculations, respectively, while the floating-point rounding calculation is applied. The duplication detection using floating point range comparison is the future work, combined with the study of the 2D GNN output behavior under the ongoing training process. | |||||||||||||
| 言語 | en | |||||||||||||
| 書誌情報 |
en : International Journal of Advanced Computer Science and Applications 巻 16, 号 5, p. 877-889, 発行日 2025 |
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| 出版者 | ||||||||||||||
| 出版者 | The Science and Information (SAI) Organization Limited | |||||||||||||
| 言語 | en | |||||||||||||
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| 収録物識別子タイプ | EISSN | |||||||||||||
| 収録物識別子 | 21565570 | |||||||||||||
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| 収録物識別子タイプ | ISSN | |||||||||||||
| 収録物識別子 | 2158107X | |||||||||||||
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| 関連タイプ | isVersionOf | |||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.14569/IJACSA.2025.0160586 | |||||||||||||
| 著者版フラグ | ||||||||||||||
| 出版タイプ | VoR | |||||||||||||
