Learning in Memristive Neural Network Architectures Using Analog Backpropagation Circuits
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Online Publication Date2018-09-20
Print Publication Date2019-02
Permanent link to this recordhttp://hdl.handle.net/10754/631342
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AbstractThe 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-based memristor models. The application level validations of the system are done using XOR problem, MNIST character, and Yale face image databases.
CitationKrestinskaya O, Salama KN, James AP (2019) Learning in Memristive Neural Network Architectures Using Analog Backpropagation Circuits. IEEE Transactions on Circuits and Systems I: Regular Papers 66: 719–732. Available: http://dx.doi.org/10.1109/TCSI.2018.2866510.