Graph Neural Network Output for Dataset Duplication Detection on Analog Integrated Circuit Recognition System
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| Опубликовано в:: | International Journal of Advanced Computer Science and Applications vol. 16, no. 5 (2025) |
|---|---|
| Главный автор: | |
| Опубликовано: |
Science and Information (SAI) Organization Limited
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| Предметы: | |
| Online-ссылка: | Citation/Abstract Full Text - PDF |
| Метки: |
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MARC
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| 001 | 3222641073 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2158-107X | ||
| 022 | |a 2156-5570 | ||
| 024 | 7 | |a 10.14569/IJACSA.2025.0160586 |2 doi | |
| 035 | |a 3222641073 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a PDF | |
| 245 | 1 | |a Graph Neural Network Output for Dataset Duplication Detection on Analog Integrated Circuit Recognition System | |
| 260 | |b Science and Information (SAI) Organization Limited |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Digital electronics | ||
| 653 | |a Datasets | ||
| 653 | |a Circuit design | ||
| 653 | |a Big Data | ||
| 653 | |a Integrated circuits | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Recognition | ||
| 653 | |a Graph neural networks | ||
| 653 | |a Floating point arithmetic | ||
| 653 | |a Analog circuits | ||
| 653 | |a Representations | ||
| 653 | |a Simulation | ||
| 653 | |a Accuracy | ||
| 653 | |a Computer science | ||
| 653 | |a Graphs | ||
| 653 | |a Neural networks | ||
| 653 | |a Computer engineering | ||
| 653 | |a Automation | ||
| 773 | 0 | |t International Journal of Advanced Computer Science and Applications |g vol. 16, no. 5 (2025) | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3222641073/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3222641073/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |