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GENIE: GENetIc Algorithm-Based REliability Assessment Methodology for Deep Neural Networks

author
Nazari, Samira
Taheri, Mahdi
Azarpeyvand, Ali
Afsharchi, Mohsen
Herglotz, Christian
Jenihhin, Maksim
statement of authorship
Samira Nazari, Mahdi Taheri, Ali Azarpeyvand, Mohsen Afsharchi, Christian Herglotz, Maksim Jenihhin
source
2025 11th International Conference on Computing and Artificial Intelligence : ICCAI 2025 : 28-31 March 2025, Kyoto, Japan : proceedings
location of publication
Piscataway
publisher
IEEE
year of publication
2025
pages
p. 264-271
conference name, date
11th International Conference on Computing and Artificial Intelligence, ICCAI 2025, 28-31 March 2025
conference location
Kyoto
url
https://doi.org/10.1109/ICCAI66501.2025.00049
subject term
tehisnärvivõrgud
mäluseadmed
algoritmid
töökindlus
Scopus
Article at Scopus
keyword
deep neural networks
DNN accelerator
memory overhead
parallel processing
reliability
ISBN
9798331524913
notes
Bibliogr.: 15 ref
Open Access
Open Access
scientific publication
teaduspublikatsioon
classifier
3.1
TalTech department
arvutisüsteemide instituut
language
inglise
Nazari, S., Taheri, M., Azarpeyvand, A., Afsharchi, M., Herglotz, C., Jenihhin, M. GENIE: GENetIc Algorithm-Based REliability Assessment Methodology for Deep Neural Networks // 2025 11th International Conference on Computing and Artificial Intelligence : ICCAI 2025 : 28-31 March 2025, Kyoto, Japan : proceedings. Piscataway : IEEE, 2025. p. 264-271. https://doi.org/10.1109/ICCAI66501.2025.00049