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Abstract
This paper explores the use of volatile resistive random access memory (RRAM) devices for implementing memristive tonotopic mapping, inspired by the human auditory system. The researchers demonstrate logarithmic integration and tonotopic mapping of signals using a generalized stochastic device-level approach with volatile RRAM devices. The tonotopic classification is shown to be suitable for speech recognition. This work suggests that memristive devices are promising for energy-efficient, high-density neuromorphic systems capable of processing temporal signals.
Publisher
Nature Communications
Published On
Apr 01, 2024
Authors
Alessandro Milozzi, Saverio Ricci, Daniele Ielmini
Tags
RRAM
memristive devices
tonotopic mapping
speech recognition
neuromorphic systems
energy-efficient
logarithmic integration
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