Practical Gradient-Descent for Memristive Crossbars

Abstract — This paper discusses implementations of gradient-descent 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. There is a significant interest in using memristive devices for computation, in particular in the context of neuromorpic systems [1] and artificial neural networks [2-7]. Memristors are typically fabricated in the form of highly-dense crossbar arrays