Analog Backpropagation Learning Circuits for Memristive Crossbar Neural Networks
Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Date
2018-05-04Online Publication Date
2018-05-04Print Publication Date
2018-05Permanent link to this record
http://hdl.handle.net/10754/630386
Metadata
Show full item recordAbstract
The implementation of backpropagation algorithm using gradient descent operation with analog circuits is an open problem. In this paper, we present the analog learning circuits for realizing backpropagation algorithm for use with neural networks in memristive crossbar arrays. The circuits are simulated in SPICE using TSMC 180nm CMOS process models, and HP memristor models. The gradient descent operations are validated comprehensively using the relevant transfer characteristics and transient response of individual circuit modules.Citation
Krestinskaya O, Salama KN, James AP (2018) Analog Backpropagation Learning Circuits for Memristive Crossbar Neural Networks. 2018 IEEE International Symposium on Circuits and Systems (ISCAS). Available: http://dx.doi.org/10.1109/iscas.2018.8351344.Additional Links
https://ieeexplore.ieee.org/document/8351344/ae974a485f413a2113503eed53cd6c53
10.1109/iscas.2018.8351344