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Nat Commun. 2018 Jun 28;9(1):2514. doi: 10.1038/s41467-018-04933-y.

Neuromorphic computing with multi-memristive synapses.

Author information

1
IBM Research - Zurich, Säumerstrasse 4, 8803, Rüschlikon, Switzerland. ibo@zurich.ibm.com.
2
Microelectronic Systems Laboratory, EPFL, Bldg ELD, Station 11, CH-1015, Lausanne, Switzerland. ibo@zurich.ibm.com.
3
IBM Research - Zurich, Säumerstrasse 4, 8803, Rüschlikon, Switzerland.
4
Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
5
Microelectronic Systems Laboratory, EPFL, Bldg ELD, Station 11, CH-1015, Lausanne, Switzerland.
6
IBM Research - Zurich, Säumerstrasse 4, 8803, Rüschlikon, Switzerland. ase@zurich.ibm.com.

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.

PMID:
29955057
PMCID:
PMC6023896
DOI:
10.1038/s41467-018-04933-y
[Indexed for MEDLINE]
Free PMC Article

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