Neuromorphic computing with multi-memristive synapses
Irem Boybat, Manuel Le Gallo, Nandakumar S. R., Timoleon Moraitis, Thomas Parnell, Tomas Tuma, Bipin Rajendran, Yusuf Leblebici, Abu Sebastian and Evangelos Eleftheriou
Received: 8th November 17
Brain-inspired neuromorphic computing has recently emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could be used to efficiently represent the strength of synaptic connections between the neuronal nodes in neural networks. However, precise modulation of the device conductance over a wide dynamic range, necessary to maintain high network accuracy, is proving to be a challenging task, primarily due to the physical mechanisms that underlie the operation of these devices. To address this challenge, we present a generic multi-memristive synaptic architecture with an efficient global clock-based arbitration scheme. We show that this concept is applicable to both artificial neural networks (ANN) and spiking neural networks (SNN). Experimental results involving over a million phase change memory devices are presented where an SNN with multi-memristive synapses is used for unsupervised learning of temporal correlations between event-based data streams. The work presented opens a pathway for realizing large-scale neural networks using memristive technology, and represents a significant step towards the realization of energy-efficient neuromorphic computing systems.
Read in full at arXiv.
This is an abstract of a preprint hosted on an independent third party site. It has not been peer reviewed but is currently under consideration at Nature Communications.