Zero-memory-overhead clipping-based fault tolerance for LSTM deep neural networks

author
Parchekani, Bahram
Nazari, Samira
Ahmadilivani, Mohammad Hasan
Azarpeyvand, Ali
Raik, Jaan
Ghasempouri, Tara
Daneshtalab, Masoud
statement of authorship
Bahram Parchekani, Samira Nazari, Mohammad Hasan Ahmadilivani, Ali Azarpeyvand, Jaan Raik, Tara Ghasempouri and Masoud Daneshtalab
source
37th IEEE International Symposium on Defect and Fault Tolerancein VLSI and Nanotechnology Systems, Harwell, Oxfordshire, Didcot, UK, October 8th - 10th, 2024
location of publication
Piscataway, New Jersey
publisher
IEEE
year of publication
2024
pages
4 p. : ill
conference name, date
37th IEEE Iternational Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, 8-10 October, 2024
conference location
Harwell, Oxfordshire, Didcot, UK
url
https://doi.org/10.1109/DFT63277.2024.10753533
subject term
tõrketaluvus
riistvara
töökindlus
tervishoid
tehisnärvivõrgud
Scopus
https://www.scopus.com/sourceid/21101123252
https://www.scopus.com/record/display.uri?eid=2-s2.0-85212407196&origin=resultslist&sort=plf-f&src=s&sot=b&sdt=b&s=ALL%28%22Zero-Memory-Overhead+Clipping-Based+Fault+Tolerance+for+LSTM+Deep+Neural+Networks%22%29&sessionSearchId=fd90d0e9983d9a88c6a21e7c6aafecbb&relpos=0
keyword
fault tolerance
hardware reliability
LSTMs
neural networks
healthcare
ISSN
2576-1501
ISBN
979-835036688-4
notes
Bibliogr.: 18 ref
scientific publication
teaduspublikatsioon
classifier
3.1
TalTech department
arvutisüsteemide instituut
language
inglise