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Journal articles
Oxide based resistive memories for low power embedded applications and neuromorphic systems
Abstract : Oxide based resistive memories (OxRAMs) is one of the potential candidates for non-volatile logic circuits and neuromorphic circuits in the applications of wearable devices, internet of things (IoT), cloud computing, and big-data processing. One of the main OxRAMs issue is the noise behavior of the high resistance state (HRS). In this work, we will demonstrate a hybrid (CMOS logic plus ReRAM devices) Non Volatile Flip Flop designed to face OxRAM variability. Concerning neuromorphic circuits, we will focus on the impact of resistance variability on the performance of Convolutional Neural Network (CNN) systems for visual pattern recognition applications.
Keywords :
Big data
Computation theory
Data handling
Embedded systems
Flip flop circuits
Networks (circuits)
Neural networks
Pattern recognition
Pattern recognition systems
Convolutional neural network
Embedded application
High-resistance state
Internet of Things (IOT)
Neuromorphic circuits
Neuromorphic systems
Non-volatile flip-flops
Visual pattern recognition
Logic circuits
https://hal-cea.archives-ouvertes.fr/cea-01837009
Contributor : Léna Le Roy Connect in order to contact the contributor
Submitted on : Thursday, July 12, 2018 - 3:52:07 PM
Last modification on : Thursday, February 17, 2022 - 10:08:06 AM
Contributor : Léna Le Roy Connect in order to contact the contributor
Submitted on : Thursday, July 12, 2018 - 3:52:07 PM
Last modification on : Thursday, February 17, 2022 - 10:08:06 AM