Modeling gate-level abstraction hierarchy using graph convolutional neural networks to predict functional de-rating factors
author
Balakrishnan, Aneesh
Lange, Thomas
Glorieux, Maximilien
Alexandrescu, Dan
Jenihhin, Maksim
statement of authorship
Aneesh Balakrishnan, Thomas Lange, Maximilien Glorieux, Dan Alexandrescu, Maksim Jenihhi
source
2019 NASA/ESA conference on adaptive hardware and systems AHS 2019 : proceedings
publisher
IEEE
year of publication
2019
pages
p. 72-78 : ill
conference name, date
The 2019 NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2019), 22-24 July 2019
conference location
Colchester, United Kingdom
url
https://doi.org/10.1109/AHS.2019.00007
subject term
ekspertsüsteemid
tehisintellekt
mudeliteooria
keyword
Probabilistic Graph Model (PGM)
Graph Convolutional Neural Network (GCN)
functional de-rating
Single Event Upset (SEU)
gate-level netlist
Graph Modeling Language (GML)
notes
Bibliogr.: 7 ref
TalTech department
arvutisüsteemide instituut
language
inglise
Reserch Group
Centre for trustworthy and efficient computing hardware (TECH)
Centre of dependable computing systems