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1 Ergebnisse
1
A Machine Learning-driven EDAC Method for Space-Application..:
, In:
2023 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT)
,
Chen, Junchao
;
Andjelkovic, Marko
;
Krstic, Milos
. - p. 1-6 , 2023
Link:
https://doi.org/10.1109/DFT59622.2023.10313560
RT T1
2023 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT)
: T1
A Machine Learning-driven EDAC Method for Space-Application Memory
UL https://suche.suub.uni-bremen.de/peid=ieee-10313560&Exemplar=1&LAN=DE A1 Chen, Junchao A1 Andjelkovic, Marko A1 Krstic, Milos A1 Luis, Vargas Fabian YR 2023 SN 2765-933X K1 Adaptation models K1 Redundancy K1 Space radiation K1 Machine learning K1 Predictive models K1 Very large scale integration K1 Probability K1 Error Detection and Correction K1 Memory Reliability K1 Machine Learning K1 Space Environment SP 1 OP 6 LK http://dx.doi.org/https://doi.org/10.1109/DFT59622.2023.10313560 DO https://doi.org/10.1109/DFT59622.2023.10313560 SF ELIB - SuUB Bremen
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