test_moissonnage_selune
type: Agent

Emerging Resistive Memory Technology for Neuromorphic Systems and Applications
type: Distribution
access URL: https://theses.hal.science/tel-00935190
format: HTML
issued: 2026-05-07T07:11:37.487910
modified: 2026-05-07T07:11:37.457994

Emerging Resistive Memory Technology for Neuromorphic Systems and Applications
type: Dataset
contact point: Organization
  fn: CCSD
keyword: extraction-de-motifs
keyword: cognitive-computing
keyword: theses
keyword: neuromorphic-computing
keyword: spiotherengineering-sciences-physicsother
keyword: infoeu-reposemanticsdoctoralthesis
keyword: phase-change-memory-pcm
keyword: resistive-oxide-memor
keyword: synapse-de-memoire-resistive
keyword: reseau-de-neurones-artificiels
keyword: informatique-cognitive-visuelle-et-auditive
keyword: spiking-neural-networks
keyword: memoire-a-changement-de-phase
keyword: conductive-bridge-memory-cbram
landing page: https://theses.hal.science/tel-00935190
description: Research in the field of neuromorphic- and cognitive- computing has generated a lot of interest in recent years. With potential application in fields such as large-scale data driven computing, robotics, intelligent autonomous systems to name a few, bio-inspired computing paradigms are being investigated as the next generation (post-Moore, non-Von Neumann) ultra-low power computing solutions. In this work we discuss the role that different emerging non-volatile resistive memory technologies (RRAM), specifically (i) Phase Change Memory (PCM), (ii) Conductive-Bridge Memory (CBRAM) and Metal-Oxide based Memory (OXRAM) can play in dedicated neuromorphic hardware. We focus on the emulation of synaptic plasticity effects such as long-term potentiation (LTP), long term depression (LTD) and spike-timing dependent plasticity (STDP) with RRAM synapses. We developed novel low-power architectures, programming methodologies, and simplified STDP-like learning rules, optimized specifically for some RRAM technologies. We show the implementation of large-scale energy efficient neuromorphic systems with two different approaches (i) deterministic multi-level synapses and (ii) stochastic-binary synapses. Prototype applications such as complex visual- and auditory- pattern extraction are also shown using feed-forward spiking neural networks (SNN). We also introduce a novel methodology to design low-area efficient stochastic neurons that exploit intrinsic physical effects of CBRAM devices.
identifier: NNT: 2013GRENT023
issued: 2026-05-07T07:11:37.476997
language: fr
modified: 2026-05-07T07:11:37.477002