Aging-Induced Failure Prognosis via Digital Sensors
| dc.contributor.author | Anik, Md Toufiq Hasan | |
| dc.contributor.author | Reefat, Hasin Ishraq | |
| dc.contributor.author | Danger, Jean-Luc | |
| dc.contributor.author | Guilley, Sylvain | |
| dc.contributor.author | Karimi, Naghmeh | |
| dc.date.accessioned | 2023-06-07T19:37:17Z | |
| dc.date.available | 2023-06-07T19:37:17Z | |
| dc.date.issued | 2023-06-05 | |
| dc.description | Great Lakes Symposium on VLSI 2023, Knoxville, TN, USA, June 5 - 7, 2023 | en_US |
| dc.description.abstract | Aggressive scaling continues to push technology into smaller feature sizes and results in more complex systems in a single chip. With such scaling, various robustness concerns have come into account among which the change of circuits’ properties during their lifetime, so-called device aging, has received a lot of attention. Due to aging, the electrical behavior of transistors deviates from its original intended one resulting in degrading the chip’s performance, and ultimately the chip fails to provide correct outputs. Thereby, prognosis of circuit performance degradation during the runtime, before the chip actually fails is highly crucial in increasing the reliability of chips. Accordingly in this paper, we develop a machine-learning based framework that, leveraging the outcome of embedded time-to-digital-convertors (so-called “digital sensors”), predicts aging-induced degradation. This information can be used to prevent chip failures via deploying Dynamic Voltage and Frequency Scaling (DVFS). | en_US |
| dc.description.uri | https://redirect.cs.umbc.edu/~nkarimi/papers/GLSVLSI23.pdf | en_US |
| dc.format.extent | 6 pages | en_US |
| dc.genre | conference papers and proceedings | en_US |
| dc.genre | preprints | en_US |
| dc.identifier | doi:10.13016/m2jqza-enk6 | |
| dc.identifier.uri | https://doi.org/10.1145/3583781.3590204 | |
| dc.identifier.uri | http://hdl.handle.net/11603/28127 | |
| dc.language.iso | en_US | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | en_US |
| dc.title | Aging-Induced Failure Prognosis via Digital Sensors | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0001-9302-413X | en_US |
| dcterms.creator | https://orcid.org/0009-0000-6776-2542 | en_US |
| dcterms.creator | https://orcid.org/0000-0002-5825-6637 | en_US |