Neuromorphic computing with multi-memristive synapses.
Nat Commun
; 9(1): 2514, 2018 06 28.
Article
in En
| MEDLINE
| ID: mdl-29955057
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.
Full text:
1
Database:
MEDLINE
Main subject:
Neural Networks, Computer
/
Biomimetic Materials
/
Electronics
/
Unsupervised Machine Learning
/
Models, Neurological
Type of study:
Prognostic_studies
Limits:
Animals
/
Humans
Language:
En
Year:
2018
Type:
Article