Effect of weight overlap region on neuromorphic system with memristive synaptic devices

Geun Ho Lee, Tae-Hyeon Kim, Min Suk Song, Jinwoo Park, Sungjoon Kim, Kyungho Hong, Yoon Kim, Byung-Gook Park and Hyungjin Kim

Chaos, Solitons & Fractals, 2022, vol. 157, issue C

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/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.

Keywords: Memristor; Neural network; Neuromorphic system; On-chip learning; Weight overlap region (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:157:y:2022:i:c:s0960077922002090

DOI: 10.1016/j.chaos.2022.111999

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