<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the continuous development of neural networks, the hardware requirements are getting higher and higher, and the emergence of memristors is expected to optimize this challenge. In this paper, by studying the inter-mapping relationship between memristor arrays and neural networks, a memristor-based convolutional neural network circuit module is designed, a binarization method is used to quantize the neural network, an acceleration module is constructed based on multiple computational arrays, and an additive tree is used to accumulate the intermediate results of the output values of multiple computational units. Simulation experiments were conducted with the help of simulation software to compare and analyze the memristor-based CNN with other neural networks. Compared with LSTM, RNN, and ELM, the accuracy of the memristor-based CNN is 4.17%, 7.48%, and 2.01% higher compared to other neural networks, respectively. In the performance analysis, the recognition rate of the memristor CNN is almost unaffected by the programming error and still achieves a recognition rate close to 98% in the best case. This study provides a new idea for implementing and applying convolutional neural networks in memristor arrays, which is expected to promote the further development of memristor neuromorphic computing.</p>
</abstract>

With the continuous development of neural networks, the hardware requirements are getting higher and higher, and the emergence of memristors is expected to optimize this challenge. In this paper, by studying the inter-mapping relationship between memristor arrays and neural networks, a memristor-based convolutional neural network circuit module is designed, a binarization method is used to quantize the neural network, an acceleration module is constructed based on multiple computational arrays, and an additive tree is used to accumulate the intermediate results of the output values of multiple computational units. Simulation experiments were conducted with the help of simulation software to compare and analyze the memristor-based CNN with other neural networks. Compared with LSTM, RNN, and ELM, the accuracy of the memristor-based CNN is 4.17%, 7.48%, and 2.01% higher compared to other neural networks, respectively. In the performance analysis, the recognition rate of the memristor CNN is almost unaffected by the programming error and still achieves a recognition rate close to 98% in the best case. This study provides a new idea for implementing and applying convolutional neural networks in memristor arrays, which is expected to promote the further development of memristor neuromorphic computing.

Research on the construction method of neural network model based on memristors

Hunan University of Science and Engineering

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Research on the construction method of neural network model based on memristors"}

With the continuous development of neural networks, the hardware requirements are getting higher and higher, and the emergence of memristors is expected to...

Research on the construction method of neural network model based on memristors

Publié en ligne: 09 sept. 2023

Pages: -

Reçu: 28 nov. 2022

Accepté: 20 mars 2022

DOI: https://doi.org/10.2478/amns.2023.2.00329

Mots clés
Memristor, Convolutional neural network, Binarization method, Acceleration module, Programming error

© 2023 Rong Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the construction method of neural network model based on memristors

Publié en ligne: 09 sept. 2023

Pages: -

Reçu: 28 nov. 2022

Accepté: 20 mars 2022

DOI: https://doi.org/10.2478/amns.2023.2.00329

Mots clésMemristor, Convolutional neural network, Binarization method, Acceleration module, Programming error

© 2023 Rong Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Memristor, Convolutional neural network, Binarization method, Acceleration module, Programming error