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On the impact of OxRAM-based synapses variability on convolutional neural networks performance

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.
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https://hal-cea.archives-ouvertes.fr/cea-01839851
Contributor : Léna Le Roy <>
Submitted on : Monday, July 16, 2018 - 10:05:43 AM
Last modification on : Monday, July 20, 2020 - 9:12:06 AM

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D. Garbin, E. Vianello, O. Bichler, M. Azzaz, Q. Rafhay, et al.. On the impact of OxRAM-based synapses variability on convolutional neural networks performance. 2015 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH´15), Jul 2015, Boston, MA, United States. pp.193-198, ⟨10.1109/NANOARCH.2015.7180611⟩. ⟨cea-01839851⟩

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