Abstract
Modern computing applications increasingly rely on technologies in artificial intelligence, machine learning, and big data analytics. These applications often demand more powerful and energy-efficient hardware. Resistive switching random access memory (ReRAM) has emerged as a promising solution to satisfy both the storage and computing needs. In this paper, a natural organic honey film embedded with carbon nanotube (CNT) was fabricated into a resistive switching device, and the resistive switching behaviors were investigated. Endurance test results show the cycle-to-cycle variation of set and reset voltages. On/Off ratio in retention test was found to be in the order of ~105 which proves its potential as a non-volatile memory device to support neuromorphic computing applications. This research opens up opportunities to execute big data and machine learning applications with modest energy consumption and minimal electronic waste.
Recommended Citation
M. M. Hasan Tanim et al., "Honey-CNT based Resistive Switching Device for Neuromorphic Computing Applications," Proceedings 2022 IEEE ACM 9th International Conference on Big Data Computing Applications and Technologies Bdcat 2022, pp. 182 - 183, Institute of Electrical and Electronics Engineers, Jan 2022.
The definitive version is available at https://doi.org/10.1109/BDCAT56447.2022.00034
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
energy-efficient computing; machine learning; Neuromorphic computing; nonvolatile memory; resistive switching
International Standard Book Number (ISBN)
978-166546090-3
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2022
Comments
National Science Foundation, Grant ECCS-2104976