DocumentCode
3455569
Title
On the impact of OxRAM-based synapses variability on convolutional neural networks performance
Author
Garbin, D. ; Vianello, E. ; Bichler, O. ; Azzaz, M. ; Rafhay, Q. ; Candelier, P. ; Gamrat, C. ; Ghibaudo, G. ; DeSalvo, B. ; Perniola, L.
Author_Institution
LETI, CEA, Grenoble, France
fYear
2015
fDate
8-10 July 2015
Firstpage
193
Lastpage
198
Abstract
In this work, both temporal (cycle-to-cycle) and spatial (device-to-device) variability of hafnium oxide based OxRAM cells are investigated at array level. The impact of the resistance variability on OxRAM-based convolutional neural network is then evaluated. Two different types of neurons, analog and digital, are considered. Results show that the studied architecture is strongly immune to both temporal and spatial variability.
Keywords
hafnium compounds; neural nets; random-access storage; HfO; OxRAM-based synapses; convolutional neural networks; cycle-to-cycle variability; device-to-device variability; spatial variability; temporal variability; Decision support systems; Driver circuits; Nanoscale devices; Neurons; Convolutional Neural Network; OxRAM; resistive memory; synapse; variability;
fLanguage
English
Publisher
ieee
Conference_Titel
Nanoscale Architectures (NANOARCH), 2015 IEEE/ACM International Symposium on
Conference_Location
Boston, MA
Type
conf
DOI
10.1109/NANOARCH.2015.7180611
Filename
7180611
Link To Document