Learning in Memristive Neural Network Architectures Using Analog Backpropagation Circuits | IEEE Journals & Magazine | IEEE Xplore

Learning in Memristive Neural Network Architectures Using Analog Backpropagation Circuits


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...Show More

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 TiO2-based memristor models. The application level validations of the system are done using XOR problem, MNIST character, and Yale face image databases.
Published in: IEEE Transactions on Circuits and Systems I: Regular Papers ( Volume: 66, Issue: 2, February 2019)
Page(s): 719 - 732
Date of Publication: 19 September 2018

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