Balakrishnan, A., Lange, T., Glorieux, M., Alexandrescu, D., Jenihhin, M.

full record print

Composing graph theory and deep neural networks to evaluate SEU type soft error effects

author

Balakrishnan, Aneesh

Lange, Thomas

Glorieux, Maximilien

Alexandrescu, Dan

Jenihhin, Maksim

statement of authorship

Aneesh Balakrishnan, Thomas Lange, Maximilien Glorieux, Dan Alexandrescu, Maksim Jenihhin

source

9th Mediterranean Conference on Embedded Computing (MECO'2020), Budva, Montenegro, 8-11 June 2020

location of publication

Danvers

publisher

IEEE

year of publication

2020

conference name, date

9th Mediterranean Conference on Embedded Computing (MECO'2020), 8-11 June 2020

conference location

Budva, Montenegro

url

https://doi.org/10.1109/MECO49872.2020.9134279

notes

Bibliogr.: 10 ref

TTÜ department

arvutisüsteemide instituut

language

inglise

Märksõnad ja võtmesõnad

subject term

tehisnärvivõrgud

diagnostika (tehnika)

veaavastus

keyword

GraphSAGE (Graph Based Neural Network)

gate-level circuit abstraction

deep neural networks

Functional Failure Rate (FFR)

Single Event Upset (SEU)

Single Event Transient (SET) and Soft Errors

Reserch Group

Centre for trustworthy and efficient computing hardware (TECH)

Centre of dependable computing systems

Balakrishnan, A., Lange, T., Glorieux, M., Alexandrescu, D., Jenihhin, M. Composing graph theory and deep neural networks to evaluate SEU type soft error effects // 9th Mediterranean Conference on Embedded Computing (MECO'2020), Budva, Montenegro, 8-11 June 2020. Danvers : IEEE, 2020. https://doi.org/10.1109/MECO49872.2020.9134279