Practical Gradient-Descent for Memristive Crossbars

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


This paper discusses implementations of gradientdescent based learning algorithms on memristive crossbar arrays. The Unregulated Step Descent (USD) is described as a practical algorithm for feed-forward on-line training of large crossbar arrays. It allows fast feed-forward fully parallel on-line hardware based learning, without requiring accurate models of the memristor behaviour and precise control of the programming pulses. The effect of device parameters, training parameters, and device variability on the learning performance of crossbar arrays trained using the USD algorithm has been studied via simulations.

Bibliographical metadata

Original languageEnglish
Title of host publicationInternational Conference on Memristive Systems, MEMRISYS 2015
ISBN (Electronic)978-1-4673-9209-9
Publication statusPublished - Nov 2015
EventInternational Conference on Memristive Systems, MEMRISYS 2015 -
Event duration: 1 Jan 1824 → …


ConferenceInternational Conference on Memristive Systems, MEMRISYS 2015
Period1/01/24 → …