Nowadays, Convolutional Neural Networks (CNNs) are exploited to solve different tasks, but their increasing complexity means an increase in the power consumption of these architectures which limits their applications. The introduction of Spiking Neural Networks (SNNs) is an important step to overcome this limit. They work in the same way as the behaviour of our brain and they are organized in layers of biological neurons, which receives spikes, elaborate them and solve the task. Spikes mean a reduction of the complexity of the operation, due to the substitution of the Multiply and Ac-cumulate operation with a simple Select and Accumulate, which is traduced into a reduction of the computational power.