AdAM: Adaptive Approximate Multiplier for Fault Tolerance in DNN Accelerators
author
Taheri, Mahdi
Cherezova, Natalia
Nazari, Samira
Azarpeyvand, Ali
Ghasempouri, Tara
Daneshtalab, Masoud
Raik, Jaan
Jenihhin, Maksim
statement of authorship
Mahdi Taheri, Natalia Cherezova, Samira Nazari, Ali Azarpeyvand, Tara Ghasempouri, Masoud Daneshtalab, Jaan Raik, and Maksim Jenihhin
source
IEEE transactions on device and materials reliability
publisher
IEEE
journal volume number month
vol. 25, no. 1
year of publication
2024
pages
p. 66-75 : ill
url
https://doi.org//10.1109/TDMR.2024.3523386
subject term
tehisnärvivõrgud
kiirendid
elektriahelad
tõrketaluvus
keyword
Deep neural networks
approximate computing
circuit design
reliability
DNN accelerator
ISSN
1530-4388
notes
Bibliogr.: 43 ref
scientific publication
teaduspublikatsioon
classifier
1.1
Scopus
https://www.scopus.com/sourceid/26049
https://www.scopus.com/pages/publications/105001086760?origin=resultslist
WOS
https://jcr.clarivate.com/jcr-jp/journal-profile?journal=IEEE%20T%20DEVICE%20MAT%20RE&year=2024
https://www.webofscience.com/wos/woscc/full-record/WOS:001449689000004
category (general)
Engineering
Tehnika
Materials science
Materjaliteadus
category (sub)
Engineering. Safety, risk, reliability and quality
Tehnika. Ohutus, risk, töökindlus ja kvaliteet
Engineering. Electrical and electronic engineering
Tehnika. Elektri- ja elektroonikatehnika
Materials science. Electronic, optical and magnetic materials
Materjaliteadus. Elektroonilised, optilised ja magnetilised materjalid
TalTech department
arvutisüsteemide instituut
language
inglise
Reserch Group
Centre for trustworthy and efficient computing hardware (TECH)
Centre of dependable computing systems