Low‐Voltage Electrochemical LixWO3 Synapses with Temporal Dynamics for Spiking Neural Networks

Low‐Voltage Electrochemical LixWO3 Synapses with Temporal Dynamics for Spiking Neural Networks

Neuromorphic computing has the great potential to enable faster and more energy‐efficient computing by overcoming the von Neumann bottleneck. However, most emerging nonvolatile memory (NVM)‐based artificial synapses suffer from insufficient precision, nonlinear synaptic weight update, high write vol...

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Bibliographic Details
Main Authors: Qingzhou Wan, Marco Rasetto, Mohammad T. Sharbati, John R. Erickson, Sridhar Reddy Velagala, Matthew T. Reilly, Yiyang Li, Ryad Benosman, Feng Xiong
Format: Article
Language:English
Published: Wiley 2021-09-01
Series:Advanced Intelligent Systems
Subjects:
dynamic synapses
electrochemical synapses
neuromorphic computing
spatiotemporal dynamics
spiking neural networks
tungsten oxide
Online Access:https://doi.org/10.1002/aisy.202100021

Internet

https://doi.org/10.1002/aisy.202100021

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