Original ResearchApplied Artificial Intelligence Letters
Energy-efficient event-based vision with neuromorphic recurrent architectures
Daniel Osei, Lucas Ferreira
Event cameras generate sparse, asynchronous data streams well suited to neuromorphic processing. We propose a recurrent spiking architecture that achieves competitive accuracy on gesture and optical-flow benchmarks while consuming an order of magnitude less energy than conventional convolutional baselines on equivalent hardware.