print

Special session: approximation and fault resiliency of DNN accelerators

author
Ahmadilivani, Mohammad Hasan
Barbareschi, Mario
Barone, Salvatore
Bosio, Alberto
Daneshtalab, Masoud
Torca, Salvatore Della
Gavarini, Gabriele
Jenihhin, Maksim
Raik, Jaan
Taheri, Mahdi
statement of authorship
Mohammad Hasan Ahmadilivani, Mario Barbareschi, Salvatore Barone, Alberto Bosio, Masoud Daneshtalab, Salvatore Della Torca, Gabriele Gavarini, Maksim Jenihhin, Jaan Raik, Annachiara Ruospo, Ernesto Sanchez, Mahdi Taheri
source
Proceedings 2023 IEEE 41st VLSI Test Symposium (VTS)
location of publication
Danvers, MA
publisher
IEEE
year of publication
2023
pages
10 p. : ill
conference name, date
41st IEEE VLSI Test Symposium, VTS 2023, 24 April-26 April 2023
conference location
San Diego
url
https://doi.org/10.1109/VTS56346.2023.10140043
subject term
testimine
kompuutersimulatsioon
usaldusväärsus
rikked
tehisnärvivõrgud
Scopus
Conference proceeding at Scopus
Article at Scopus
WOS
Article at WOS
kvartiil
Q2
category (general)
Engineering
Computer science
category (sub)
Engineering. Electrical and electronic engineering
Computer science. Computer science applications
keyword
approximate computing
deep neural networks
fault emulation
reliability
resiliency assessment
ISBN
979-835034630-5
notes
Bibliogr.: 47 ref
Open Access
Open Access
scientific publication
teaduspublikatsioon
classifier
3.1
TTÜ department
arvutisüsteemide instituut
language
inglise
Ahmadilivani, M.H., Barbareschi, M., Barone, S., Bosio, A., Daneshtalab, M., Torca, S.D., Gavarini, G., Jenihhin, M., Raik, J., Taheri, M. et al. Special session: approximation and fault resiliency of DNN accelerators // Proceedings 2023 IEEE 41st VLSI Test Symposium (VTS). Danvers, MA : IEEE, 2023. 10 p. : ill. https://doi.org/10.1109/VTS56346.2023.10140043