Recent developments of Deep Artificial Neural Networks (ANNs) have pushed forward the state-of-the-art in the field of image recognition. However, the high power demand required by these networks when it comes to perform inference tasks on edge devices limits the spread of ANNs in context where the energy/power consumption is crucial. On the other hand, Spiking Neural Networks (SNNs), due to their biologically inspired behavior, have shown promising results both in terms of power efficiency and real-time classification performance. Being the communication between neurons based on spikes, SNNs guarantee a lower computational load, as well as a reduction of latency. Along with the development of efficient SNN specialized accelerators (TrueNorth, SpiNNaker and Intel Loihi), another advancement in the field of neuromorphic hardware has come from a new generation of camera, the DVS event-based sensor.