Analog Backpropagation Learning Circuits for Memristive Crossbar Neural Networks

Olga Krestinskaya; Khaled Nabil Salama; Alex Pappachen James; IEEE

doi:10.1109/ISCAS.2018.8351344

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Analog Backpropagation Learning Circuits for Memristive Crossbar Neural Networks

Olga Krestinskaya, Khaled Nabil Salama, Alex Pappachen James and IEEE

2018 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), Vol.2018-, pp.1-5

IEEE International Symposium on Circuits and Systems

01/01/2018

DOI: https://doi.org/10.1109/ISCAS.2018.8351344

Abstract

Engineering

Engineering, Electrical & Electronic

Science & Technology

Technology

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.

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Title: Analog Backpropagation Learning Circuits for Memristive Crossbar Neural Networks
Creators - without role: Olga Krestinskaya - Nazarbayev University
Khaled Nabil Salama - King Abdullah University of Science and Technology
Alex Pappachen James - Nazarbayev University
IEEE
Publication Details: 2018 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), Vol.2018-, pp.1-5
Series: IEEE International Symposium on Circuits and Systems
Publisher: IEEE
Number of pages: 5
Identifiers: 9941330308331
Academic Unit: Imam Abdulrahman Bin Faisal University; King Abdullah University of Science & Technology
Language: English
Resource Type: Conference proceeding