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 - 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 -