Machine Learning Clustering Techniques for Selective Mitigation of Critical Design Features-Reference-Cited by-同舟云学术

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Machine Learning Clustering Techniques for Selective Mitigation of Critical Design Features

Published:2020-07 Issue: Volume: Page:
ISSN:
Container-title:2020 IEEE 26th International Symposium on On-Line Testing and Robust System Design (IOLTS)
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Author:

Lange Thomas,Balakrishnan Aneesh,Glorieux Maximilien,Alexandrescu Dan,Sterpone Luca

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9151097/9159702/09159751.pdf?arnumber=9159751

Cited by 5 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. Machine Learning Methodologies to Predict the Results of Simulation-Based Fault Injection;IEEE Transactions on Circuits and Systems I: Regular Papers;2024-05

2. Supervised Deep Learning and Classification of Single-Event Transients;IEEE Transactions on Nuclear Science;2023-08

3. Bits, Flips and RISCs;2023 26th International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS);2023-05-03

4. Towards Critical Flip-Flop Identification for Soft-Error Tolerance with Graph Neural Networks;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2023

5. Reliability assessment on 16 nm ultrascale+ MPSoC using fault injection and fault tree analysis;Microelectronics Reliability;2021-05

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