Digital Biologically Plausible Implementation of Binarized Neural Networks With Differential Hafnium Oxide Resistive Memory Arrays

Autor: Tifenn Hirtzlin, Marc Bocquet, Bogdan Penkovsky, Jacques-Olivier Klein, Etienne Nowak, Elisa Vianello, Jean-Michel Portal, Damien Querlioz

Publikováno v: Frontiers in Neuroscience, Vol 13 (2020)

The brain performs intelligent tasks with extremely low energy consumption. This work takes its inspiration from two strategies used by the brain to achieve this energy efficiency: the absence of separation between computing and memory functions and

Externí odkaz: https://doaj.org/article/dbd1743fa64545fea508526d5b5e607c

Advances in Emerging Memory Technologies: From Data Storage to Artificial Intelligence

Autor: Gabriel Molas, Etienne Nowak

Publikováno v: Applied Sciences, Vol 11, Iss 23, p 11254 (2021)

This paper presents an overview of emerging memory technologies. It begins with the presentation of stand-alone and embedded memory technology evolution, since the appearance of Flash memory in the 1980s. Then, the progress of emerging memory technol

Externí odkaz: https://doaj.org/article/3e207d91becc4bed8eb8a5e257a6ae31

Model of the Weak Reset Process in HfO x Resistive Memory for Deep Learning Frameworks

Autor: Etienne Nowak, Damien Querlioz, Marc Bocquet, Axel Laborieux, Atreya Majumdar, Elisa Vianello, Jean-Michel Portal, Tifenn Hirtzlin, Jacques-Olivier Klein

Publikováno v: IEEE Transactions on Electron Devices
IEEE Transactions on Electron Devices, 2021, 68 (10), pp.4925-4932. ⟨10.1109/TED.2021.3108479⟩
IEEE Transactions on Electron Devices, Institute of Electrical and Electronics Engineers, 2021, 68 (10), pp.4925-4932. ⟨10.1109/TED.2021.3108479⟩

The implementation of current deep learning training algorithms is power-hungry, due to data transfer between memory and logic units. Oxide-based resistive random access memories (RRAMs) are outstanding candidates to implement in-memory computing, wh

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::edcbfdd9a7b73660ad52cf15830ef24e
https://hal.science/hal-03372056/document

Ge-Se-Sb-N-based OTS scaling perspectives for high-density 1 S1R crossbar arrays

Autor: N. Castellani, G. Molas, Marc Bocquet, L. Grenouillet, J-M. Portal, L. Reganaz, Etienne Nowak, T. Magis, C. Carabasse, G. Navarro, D. Deleruyelle, Mathieu Bernard, J. Minguet Lopez

Publikováno v: 2021 IEEE International Memory Workshop (IMW)
2021 IEEE INTERNATIONAL MEMORY WORKSHOP (IMW)
2021 IEEE INTERNATIONAL MEMORY WORKSHOP (IMW), May 2021, Dresde, Germany. pp.107-110, ⟨10.1109/IMW51353.2021.9439606⟩

International audience; In this paper, we address the scalability of GeSeSbN based Ovonic Threshold Switch selector for high density crossbar integration. Impact of cell size down to 80nm on selector electrical characteristics, typical switching volt

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::94a04afe0bb92138e1c6f390ca2ddb15
https://doi.org/10.1109/imw51353.2021.9439606

Innovative Low-Power Self-Nanoconfined Phase-Change Memory

Autor: Gauthier Lefevre, C. Sabbione, Sylvain David, Etienne Nowak, J. Garrione, N. Castellani, Marie-Claire Cyrille, Anna Lisa Serra, Nicolas Bernier, Christophe Vallée, Guillaume Bourgeois, Gabriele Navarro, Mathieu Bernard, O. Cueto, Christelle Charpin-Nicolle

Publikováno v: IEEE Transactions on Electron Devices
IEEE Transactions on Electron Devices, Institute of Electrical and Electronics Engineers, 2021, 68 (2), pp.535-540. ⟨10.1109/TED.2020.3044267⟩
IEEE Transactions on Electron Devices, 2021, 68 (2), pp.535-540. ⟨10.1109/TED.2020.3044267⟩

International audience; In this paper, we demonstrate at array level and in industrial like devices, the extreme scaling down to nanometric dimensions of the Phase-Change Memory technology thanks to an innovative Self-Nano-Confined PCM device (SNC PC

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a85ded2bac29098bfe311f43f120a78b
https://hal-cea.archives-ouvertes.fr/cea-03119677/document