Experimental Demonstration of Multilevel Resistive Random Access Memory Programming for up to Two Months Stable Neural Networks Inference Accuracy

Experimental Demonstration of Multilevel Resistive Random Access Memory Programming for up to Two Months Stable Neural Networks Inference Accuracy

Crossbars of resistive memories, or memristors, provide a road to reduce the energy consumption of artificial neural networks, by naturally implementing multiply accumulate operations, their most basic calculations. However, a major challenge of implementing robust hardware neural networks is the co...

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Bibliographic Details
Main Authors: Eduardo Esmanhotto, Tifenn Hirtzlin, Djohan Bonnet, Niccolo Castellani, Jean-Michel Portal, Damien Querlioz, Elisa Vianello
Format: Article
Language:English
Published: Wiley 2022-11-01
Series:Advanced Intelligent Systems
Subjects:
in-memory computing
memristors
multilevel cells
neural networks
neuromorphic
resistive random access memories
Online Access:https://doi.org/10.1002/aisy.202200145

Internet

https://doi.org/10.1002/aisy.202200145

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