Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends

M. Lakshmi Varshika; Federico Corradi; Anup Kumar Das

doi:10.3390/electronics11101610

Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends

M. Lakshmi Varshika (Corresponding author)
, Federico Corradi (Corresponding author)
, Anup Kumar Das (Corresponding author)

Research output: Contribution to journal › Article › Academic › peer-review

30 Citations (Scopus)

237 Downloads (Pure)

Abstract

A sustainable computing scenario demands more energy-efficient processors. Neuromorphic systems mimic biological functions by employing spiking neural networks for achieving brain-like efficiency, speed, adaptability, and intelligence. Current trends in neuromorphic technologies address the challenges of investigating novel materials, systems, and architectures for enabling high-integration and extreme low-power brain-inspired computing. This review collects the most recent trends in exploiting the physical properties of nonvolatile memory technologies for implementing efficient in-memory and in-device computing with spike-based neuromorphic architectures.

Original language	English
Article number	1610
Number of pages	24
Journal	Electronics
Volume	11
Issue number	10
DOIs	https://doi.org/10.3390/electronics11101610
Publication status	Published - 18 May 2022

Keywords

nonvolatile memory
spiking neural networks
neuromorphic computing
spiking neural network (SNN)

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abstract = "A sustainable computing scenario demands more energy-efficient processors. Neuromorphic systems mimic biological functions by employing spiking neural networks for achieving brain-like efficiency, speed, adaptability, and intelligence. Current trends in neuromorphic technologies address the challenges of investigating novel materials, systems, and architectures for enabling high-integration and extreme low-power brain-inspired computing. This review collects the most recent trends in exploiting the physical properties of nonvolatile memory technologies for implementing efficient in-memory and in-device computing with spike-based neuromorphic architectures.",

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AB - A sustainable computing scenario demands more energy-efficient processors. Neuromorphic systems mimic biological functions by employing spiking neural networks for achieving brain-like efficiency, speed, adaptability, and intelligence. Current trends in neuromorphic technologies address the challenges of investigating novel materials, systems, and architectures for enabling high-integration and extreme low-power brain-inspired computing. This review collects the most recent trends in exploiting the physical properties of nonvolatile memory technologies for implementing efficient in-memory and in-device computing with spike-based neuromorphic architectures.

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Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends

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