  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.
