Journal
CHAOS SOLITONS & FRACTALS
Volume 157, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.chaos.2022.111999
Keywords
Memristor; Neural network; Neuromorphic system; On-chip learning; Weight overlap region
Categories
Funding
- NRF - Korean government [2020M3H5A1081111, 2020M3F3A2A01081656, 2021R1C1C1014530]
- MSIT (Ministry of Science and ICT) , Korea [IITP-2021-0-02052]
- Korean government [2020-0-01294]
- Brain Korea 21 Four Program
- National Research Foundation of Korea [2021R1C1C1014530] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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This study analyzes the effect of the conductance overlap region of memristors on the recognition accuracy for on-chip learning simulation.
Recently, hardware-based neural network using memristive devices, so called neuromorphic system, has been extensively studied. Especially, on-chip (in situ) learning methods where training occurs inside hardware structure itself have been proposed and optimized based on memristor crossbar arrays regarding the linearity of weight-update characteristics. In this study, we analyze the effect of conductance overlap region of memristor on the recognition accuracy for on-chip learning simulation. The effect of conductance overlap region on recognition accuracy for modified national institute of standards and technology (MNIST) dataset is studied with an identical potentiation/depression pulse applied to Pt/Al2O3/TiOx/Ti/Pt stacked memristor. The overlap range can be varied by different pulse amplitude, and the training characteristics of memristive neural network is significantly dependent on the weight-update overlap region.(c) 2022 Published by Elsevier Ltd.
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