TY - JOUR
T1 - Neuromorphic computing with multi-memristive synapses
AU - Boybat, Irem
AU - Le Gallo, Manuel
AU - Nandakumar, S. R.
AU - Moraitis, Timoleon
AU - Parnell, Thomas
AU - Tuma, Tomas
AU - Rajendran, Bipin
AU - Leblebici, Yusuf
AU - Sebastian, Abu
AU - Eleftheriou, Evangelos
N1 - Funding Information:
We would like to thank N. Papandreou, U. Egger, S. Wozniak, S. Sidler, A. Pantazi, M. BrightSky, and G. Burr for technical input. I. B. and T. M. would like to acknowledge financial support from the Swiss National Science Foundation. A. S. would like to acknowledge funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement no. 682675).
Publisher Copyright:
© 2018 The Author(s).
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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.
AB - 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.
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U2 - 10.1038/s41467-018-04933-y
DO - 10.1038/s41467-018-04933-y
M3 - Article
C2 - 29955057
AN - SCOPUS:85049333665
SN - 2041-1723
VL - 9
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 2514
ER -