Energy-efficient event-based vision with neuromorphic recurrent architectures

Abstract

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.

Keywords

• neuromorphic computing
• event cameras
• spiking neural networks