Analog Backpropagation Learning Circuits for Memristive Crossbar Neural Networks

Olga Krestinskaya, Khaled Nabil Salama, Alex Pappachen James

2018

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DOI 10.1109/iscas.2018.8351344
Data confidence 85 high
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Abstract

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.