Investigation of Analog Memristor Characteristics for Hardware Synaptic Weight in Multilayer Neural Network

Analog Memristor Characteristics The systematic design of memristor-based neural network is provided by analog conductance state parameters to accurately emulate the software-based high-resolution weight at discrete device level. The requirement of discrete analog conductance of memristor device is measured as ≈50 states with nonlinearity value of ≈0.142 within the deviation range of 5% for inference accuracy of ≈84.36% and loss value of ≈0.168. Further details can be found in article number 2400710 by Jingon Jang, Yoonseok Song, and Sungjun Park.

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Publication Details

Journal
Advanced Intelligent Systems
Published
2025-03-01
DOI
https://doi.org/10.1002/aisy.202570012
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73
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article

Investigation of Analog Memristor Characteristics for Hardware Synaptic Weight in Multilayer Neural Network

Yoonseok Song, Sungjun Park, Jingon Jang
1 citations
Advanced Intelligent Systems
Advanced Memory and Neural Computing
0.73
article

Investigation of Analog Memristor Characteristics for Hardware Synaptic Weight in Multilayer Neural Network

Yoonseok Song, Sungjun Park, Jingon Jang
article en
1 citations

Abstract

Analog Memristor Characteristics The systematic design of memristor-based neural network is provided by analog conductance state parameters to accurately emulate the software-based high-resolution weight at discrete device level. The requirement of discrete analog conductance of memristor device is measured as ≈50 states with nonlinearity value of ≈0.142 within the deviation range of 5% for inference accuracy of ≈84.36% and loss value of ≈0.168. Further details can be found in article number 2400710 by Jingon Jang, Yoonseok Song, and Sungjun Park.

Advanced Intelligent SystemsVol. 7(3)
Kwangwoon University (KR), Ajou University (KR)
Affordable and clean energy
Openalex Percentile: Top 31%
Advanced Memory and Neural Computing
0.73
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