Design of Multilayer Perceptron Network Based on Metal-Oxide Memristive Devices
| Autor: | Alexey Pimashkin, Dmitry Korolev, Alexey Belov, Victor B. Kazantsev, S.A. Shchanikov, Anton Zuev, I. A. Bordanov, S.N. Danilin, Alexey Mikhaylov |
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| Rok vydání: | 2019 |
| Předmět: |
010302 applied physics
Artificial neural network Computer science Design of experiments Computation Process (computing) 02 engineering and technology Memristor 021001 nanoscience & nanotechnology 01 natural sciences law.invention law Multilayer perceptron 0103 physical sciences Key (cryptography) Electronic engineering 0210 nano-technology Brain–computer interface |
| Zdroj: | DeSE |
| Popis: | A key problem at hardware implementation of artificial neural networks based on memristors (ANNM) is to ensure the required accuracy of their operation at the transition from models to real fabricated memristive devices. Due to a number of factors, such as the imperfections in stateof- the-art memristors and memristive arrays, ANNM design and tuning methods, additional computation errors occur during the process of ANNM hardware implementation. The article proposes a general approach to the simulation and design of a multilayer perceptron (MLP) network implemented with original cross-bar arrays of metal-oxide memristive devices. The proposed approach is based on the theory of engineering tolerances, simulation and the design of experiments. The authors present the research results for the ANNM trained to solve the problem of nonlinear classification for a bidirectional adaptive neural interface. |
| Databáze: | OpenAIRE |
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