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We present an ensemble approach for implementing Spiking Neural Networks (SNNs) with on-line unsupervised learning, well-suited for robust and energy-efficient design of neuromorphic computing systems for pattern recognition tasks. Inspired from the collective neuronal activity observed in the visual cortex, the proposed EnsembleSNN architecture involves multiple simple SNNs or ensembles acting in...
Neuromorphic algorithms, which are comprised of highly complex, large-scale networks of artificial neurons, are increasingly used for a variety of recognition, classification, search and vision tasks. However, their computational and energy requirements can be quite high, and hence their energy-efficient implementation is of great interest. We propose a new approach to design energy-efficient hardware...
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