Embracing the unreliability of memory devices for neuromorphic computing - Université de Toulon Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

Embracing the unreliability of memory devices for neuromorphic computing

Résumé

The emergence of resistive non-volatile memories opens the way to highly energy-efficient computation near-or in-memory. However, this type of computation is not compatible with conventional ECC, and has to deal with device unreliability. Inspired by the architecture of animal brains, we present a manufactured differential hybrid CMOS/RRAM memory architecture suitable for neural network implementation that functions without formal ECC. We also show that using low-energy but error-prone programming conditions only slightly reduces network accuracy.
Fichier principal
Vignette du fichier
Bocqet, IRPS2020, Embracing the Unreliability of Memory Devices for Neuromorphic Computing.pdf (1.59 Mo) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04019222 , version 1 (08-03-2023)

Identifiants

Citer

Marc Bocquet, Tifenn Hirtzlin, Jacques-Olivier Klein, Etienne Nowak, Elisa Vianello, et al.. Embracing the unreliability of memory devices for neuromorphic computing. IRPS 2020 - IEEE International Reliability Physics Symposium, IEEE, Apr 2020, Dallas (Texas), United States. pp.1-5, ⟨10.1109/IRPS45951.2020.9128346⟩. ⟨hal-04019222⟩
24 Consultations
11 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More