Learning in Memristive Neural Network Architectures Using Analog Backpropagation Circuits
The on-chip implementation of learning algorithms would speed up the training of neural networks in crossbar arrays. The circuit level design and implementation of a back-propagation algorithm using gradient descent operation for neural network architectures is an open problem. In this paper, we pro...
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| Published in: | IEEE transactions on circuits and systems. I, Regular papers Vol. 66; no. 2; pp. 719 - 732 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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New York
IEEE
01.02.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1549-8328, 1558-0806 |
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| Abstract | The on-chip implementation of learning algorithms would speed up the training of neural networks in crossbar arrays. The circuit level design and implementation of a back-propagation algorithm using gradient descent operation for neural network architectures is an open problem. In this paper, we propose analog backpropagation learning circuits for various memristive learning architectures, such as deep neural network, binary neural network, multiple neural network, hierarchical temporal memory, and long short-term memory. The circuit design and verification are done using TSMC 180-nm CMOS process models and TiO 2 -based memristor models. The application level validations of the system are done using XOR problem, MNIST character, and Yale face image databases. |
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| AbstractList | The on-chip implementation of learning algorithms would speed up the training of neural networks in crossbar arrays. The circuit level design and implementation of a back-propagation algorithm using gradient descent operation for neural network architectures is an open problem. In this paper, we propose analog backpropagation learning circuits for various memristive learning architectures, such as deep neural network, binary neural network, multiple neural network, hierarchical temporal memory, and long short-term memory. The circuit design and verification are done using TSMC 180-nm CMOS process models and TiO 2 -based memristor models. The application level validations of the system are done using XOR problem, MNIST character, and Yale face image databases. The on-chip implementation of learning algorithms would speed up the training of neural networks in crossbar arrays. The circuit level design and implementation of a back-propagation algorithm using gradient descent operation for neural network architectures is an open problem. In this paper, we propose analog backpropagation learning circuits for various memristive learning architectures, such as deep neural network, binary neural network, multiple neural network, hierarchical temporal memory, and long short-term memory. The circuit design and verification are done using TSMC 180-nm CMOS process models and TiO2-based memristor models. The application level validations of the system are done using XOR problem, MNIST character, and Yale face image databases. |
| Author | Salama, Khaled Nabil Krestinskaya, Olga James, Alex Pappachen |
| Author_xml | – sequence: 1 givenname: Olga orcidid: 0000-0001-8038-4558 surname: Krestinskaya fullname: Krestinskaya, Olga organization: Bioinspired Microelectronics Systems Group, Nazarbayev University, Astana, Kazakhstan – sequence: 2 givenname: Khaled Nabil surname: Salama fullname: Salama, Khaled Nabil organization: Sensors Lab, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia – sequence: 3 givenname: Alex Pappachen orcidid: 0000-0001-5655-1213 surname: James fullname: James, Alex Pappachen email: apj@ieee.org organization: Electrical and Computer Engineering Department, School of Engineering, Nazarbayev University, Astana, Kazakhstan |
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| SubjectTerms | Analog circuits Artificial neural networks Back propagation Back propagation networks Backpropagation Backpropagation algorithms binary neural network Biological neural networks Circuit design CMOS Computer architecture crossbar deep neural network Hardware hierarchical temporal memory learning long-short term memory Machine learning memristor Memristors multiple neural network Neural networks Titanium dioxide |
| Title | Learning in Memristive Neural Network Architectures Using Analog Backpropagation Circuits |
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