Towards Hardware Implementation of Double-Layer Perceptron Based on Metal-Oxide Memristive Nanostructures

2017/11/03 by A. N. Mikhaylov, Alexey Mikhaylov, Mikhaylov, A. N. +28
Computer Science · Engineering · Neuroscience · Physics and Astronomy · #Advanced Memory and Neural Computing #Applied Physics (physics.app-ph) #CCD and CMOS Imaging Sensors #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #FOS: Physical sciences #Neuroscience and Neural Engineering #cs.ET #physics.app-ph

paper · pdf · doi:10.48550/arxiv.1711.01041

arxiv created 2017/11/03 · openalex publication_date 2017/11/03 · arxiv updated 2017/11/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Construction and training principles have been proposed and tested for an artificial neural network based on metal-oxide thin-film nanostructures possessing bipolar resistive switching (memristive) effect. Experimental electronic circuit of neural network is implemented as a double-layer perceptron with a weight matrix composed of 32 memristive devices. The network training algorithm takes into account technological variations of the parameters of memristive nanostructures. Despite the limited size of weight matrix the developed neural network model is well scalable and capable of solving nonlinear classification problems.

Citations

• Neuromorphic electronic systems

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