  Spiking Neural Networks are a type of neural networks where neurons
communicate using only spikes. They are often presented as a low-power
alternative to classical neural networks, but few works have proven these
claims to be true. In this work, we present a metric to estimate the energy
consumption of SNNs independently of a specific hardware. We then apply this
metric on SNNs processing three different data types (static, dynamic and
event-based) representative of real-world applications. As a result, all of our
SNNs are 6 to 8 times more efficient than their FNN counterparts.
