Neuromorphic computing with multi-memristive synapses

Irem Boybat (), Manuel Le Gallo, S. R. Nandakumar, Timoleon Moraitis, Thomas Parnell, Tomas Tuma, Bipin Rajendran, Yusuf Leblebici, Abu Sebastian () and Evangelos Eleftheriou
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Irem Boybat: IBM Research - Zurich
Manuel Le Gallo: IBM Research - Zurich
S. R. Nandakumar: IBM Research - Zurich
Timoleon Moraitis: IBM Research - Zurich
Thomas Parnell: IBM Research - Zurich
Tomas Tuma: IBM Research - Zurich
Bipin Rajendran: New Jersey Institute of Technology
Yusuf Leblebici: Microelectronic Systems Laboratory, EPFL, Bldg ELD
Abu Sebastian: IBM Research - Zurich
Evangelos Eleftheriou: IBM Research - Zurich

Nature Communications, 2018, vol. 9, issue 1, 1-12

Abstract: Abstract Neuromorphic computing has 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 efficiently represent the synaptic weights in artificial neural networks. However, precise modulation of the device conductance over a wide dynamic range, necessary to maintain high network accuracy, is proving to be challenging. To address this, we present a multi-memristive synaptic architecture with an efficient global counter-based arbitration scheme. We focus on phase change memory devices, develop a comprehensive model and demonstrate via simulations the effectiveness of the concept for both spiking and non-spiking neural networks. Moreover, we present experimental results involving over a million phase change memory devices for unsupervised learning of temporal correlations using a spiking neural network. The work presents a significant step towards the realization of large-scale and energy-efficient neuromorphic computing systems.

Date: 2018
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DOI: 10.1038/s41467-018-04933-y

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