Neuromorphic computing with multi-memristive synapses

Document Type

Article

Publication Date

12-1-2018

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.

Identifier

85049333665 (Scopus)

Publication Title

Nature Communications

External Full Text Location

https://doi.org/10.1038/s41467-018-04933-y

e-ISSN

20411723

PubMed ID

29955057

Issue

1

Volume

9

Grant

682675

Fund Ref

Horizon 2020 Framework Programme

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