publication . Other literature type . Conference object . 2015 . Embargo end date: 10 Nov 2015

Practical gradient-descent for memristive crossbars

Piotr Dudek; Manu V Nair;
Open Access
  • Published: 10 Nov 2015
  • Publisher: 2015 International Conference on Memristive Systems (MEMRISYS)
  • Country: Switzerland
Abstract
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.
Subjects
free text keywords: Institute of Neuroinformatics, 570 Life sciences; biology, Computer science, Practical algorithm, Convergence (routing), Computer engineering, Crossbar switch, Implementation, Memristor, law.invention, law, Device parameters, Electronic engineering, Gradient descent
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