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Effect of weight overlap region on neuromorphic system with memristive synaptic devices.
- Source :
-
Chaos, Solitons & Fractals . Apr2022, Vol. 157, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
Abstract
- 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/Al 2 O 3 /TiO x /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. • Ti/Pt/Al 2 O 3 /TiO x /Ti/Pt stacked memristor devices for neuromorphic system • Evaluating reliability including endurance and retention characteristics • Learning characteristics depending on pulse amplitude regarding linearity and weight overlap region • Analyzing effect of learning characteristics on recognition accuracy for on-chip training [ABSTRACT FROM AUTHOR]
- Subjects :
- *ALUMINUM oxide
*MEMRISTORS
Subjects
Details
- Language :
- English
- ISSN :
- 09600779
- Volume :
- 157
- Database :
- Academic Search Index
- Journal :
- Chaos, Solitons & Fractals
- Publication Type :
- Periodical
- Accession number :
- 156101446
- Full Text :
- https://doi.org/10.1016/j.chaos.2022.111999