Abstract

Promising synaptic behavior has been exhibited by memristors based on natural organic materials. Such memristor-based neuromorphic systems offer notable benefits, including environmental sustainability, low production and disposal costs, non-volatile storage capability, and bio/Complementary Metal-Oxide-Semiconductor (CMOS) compatibility. Here, a 256-level honey memristor-based neuromorphic system is experimentally evaluated for image recognition. In detail, first, 256-level honey memristors are manufactured and tested based on in-house technology; next, the non-linear characteristics and inherent variation of honey memristor devices, which lead to imprecise weight updates and limit the inference accuracy, are investigated. Experimental results indicate that the inference accuracy of the 256-level honey memristor-based neuromorphic system is greater than 88% without cycle-to-cycle variations and 87% with cycle-to-cycle variations for different optimization algorithms. The overall performance of optimization algorithms with and without variation is compared in terms of energy and latency, where the momentum algorithm consistently outperforms the rest of the algorithms. This 256-level honey memristor is a promising alternative enabling sustainable neuromorphic systems, encouraging further research into natural organic materials for neuromorphic computing.

Department(s)

Electrical and Computer Engineering

Publication Status

Open Access

Comments

University of South Alabama, Grant 2247343

Keywords and Phrases

artificial intelligence; memristors

International Standard Serial Number (ISSN)

1350-911X; 0013-5194

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2025 The Authors, All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

01 Sep 2024

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