Analog Backpropagation Learning Circuits for Memristive Crossbar Neural Networks

Olga Krestinskaya, Khaled N. Salama, Alex Pappachen James

Research output: Chapter in Book/Report/Conference proceedingConference contribution

39 Scopus citations

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.
Original languageEnglish (US)
Title of host publication2018 IEEE International Symposium on Circuits and Systems (ISCAS)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)9781538648810
DOIs
StatePublished - May 4 2018

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