Composing Graph Theory and Deep Neural Networks to Evaluate SEU Type Soft Error Effects
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Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9125448/9134063/09134279.pdf?arnumber=9134279
Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Towards Evaluating SEU Type Soft Error Effects with Graph Attention Network;2024 2nd International Symposium of Electronics Design Automation (ISEDA);2024-05-10
2. Machine Learning Methodologies to Predict the Results of Simulation-Based Fault Injection;IEEE Transactions on Circuits and Systems I: Regular Papers;2024-05
3. On the Prediction of Hardware Security Properties of HLS Designs Using Graph Neural Networks;2023 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT);2023-10-03
4. Supervised Deep Learning and Classification of Single-Event Transients;IEEE Transactions on Nuclear Science;2023-08
5. Locating Critical-Reliability Gates for Sequential Circuits based on the Time Window Graph Model;2022 IEEE 31st Asian Test Symposium (ATS);2022-11
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