Non-Volatile Memory Devices for Neuromorphic Computing
Abstract
This chapter explores the critical role of emerging non-volatile memory (eNVM) devices in advancing neuromorphic computing. Inspired by the human brain's remarkable ability to perform complex tasks like pattern recognition and adaptation, neuromorphic computing offers immense potential for implementing complex neural networks (NNs) in real hardware. Despite ongoing research into the brain's complexities, its fundamental operation relies on neurons (processing units communicating via electrochemical signals) and synapses (junctions between neurons). The modifiable strength of the synapse is key to learning and memory functionality, allowing the brain to adapt and rewire itself. For dense and low-power circuits in neuromorphic computing, these eNVM devices are ideal due to their synaptic-like behavior and non-volatile information storage. Properties such as conductance modulation of these devices emulate synaptic plasticity, which is crucial for learning, and their scalability makes them promising for large-scale neuromorphic architectures. This chapter will delve into the various types of eNVM devices such as resistive RAM (ReRAM), phase-change memory (PCM), spintronics-based memory devices, and their applications in neuromorphic computing, discussing their challenges and opportunities, and highlighting recent advances in their integration within neuromorphic systems.
Versions
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BibTeX
@misc{das2026nonvolatile,
title = {{Non-Volatile Memory Devices for Neuromorphic Computing}},
author = {Debasis Das},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.39625},
eprint = {2610.39625},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.39625},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - Non-Volatile Memory Devices for Neuromorphic Computing
AU - Debasis Das
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.39625
UR - https://arxiv.org/abs/2610.39625
AB - This chapter explores the critical role of emerging non-volatile memory (eNVM) devices in advancing neuromorphic computing. Inspired by the human brain's remarkable ability to perform complex tasks like pattern recognition and adaptation, neuromorphic computing offers immense potential for implementing complex neural networks (NNs) in real hardware. Despite ongoing research into the brain's complexities, its fundamental operation relies on neurons (processing units communicating via electrochemical signals) and synapses (junctions between neurons). The modifiable strength of the synapse is key to learning and memory functionality, allowing the brain to adapt and rewire itself. For dense and low-power circuits in neuromorphic computing, these eNVM devices are ideal due to their synaptic-like behavior and non-volatile information storage. Properties such as conductance modulation of these devices emulate synaptic plasticity, which is crucial for learning, and their scalability makes them promising for large-scale neuromorphic architectures. This chapter will delve into the various types of eNVM devices such as resistive RAM (ReRAM), phase-change memory (PCM), spintronics-based memory devices, and their applications in neuromorphic computing, discussing their challenges and opportunities, and highlighting recent advances in their integration within neuromorphic systems.
ER -