DeepAxe : a framework for exploration of approximation and reliability trade-offs in DNN accelerators
author
Taheri, Mahdi
Riazati, Mohamad
Ahmadilivani, Mohammad Hasan
Jenihhin, Maksim
Daneshtalab, Masoud
Raik, Jaan
Sjödin, Mikael
Lisper, Björn
statement of authorship
Mahdi Taheri, Mohammad Riazati, Mohammad Hasan Ahmadilivani, Maksim Jenihhin, Masoud Daneshtalab, Jaan Raik, Mikael Sjodin, Bjorn Lisper
source
2023 24th International Symposium on Quality Electronic Design (ISQED)
location of publication
Piscataway
publisher
IEEE
year of publication
2023
pages
8 p. : ill
conference name, date
24th International Symposium on Quality Electronic Design (ISQED), 5-7 April 2023
conference location
San Francisco, USA
url
https://doi.org/10.1109/ISQED57927.2023.10129353
subject term
testimine
rikked
kompuutersimulatsioon
tehisnärvivõrgud
keyword
deep neural networks
approximate computing
fault simulation
reliability
resiliency assessment
ISSN
1948-3295
ISBN
979-8-3503-3475-3
notes
This paper is accepted at the 24th International Symposium on Quality Electronic Design (ISQED) 2023
scientific publication
teaduspublikatsioon
classifier
3.1
TalTech department
arvutisüsteemide instituut
language
inglise
Reserch Group
Centre for trustworthy and efficient computing hardware (TECH)