Nowadays traditional artificial neural networks (ANNs) present a significant challenge in edge devices and embedded systems due to their high power consumption. This inefficiency in standard machine learning (ML) and deep learning (DL) solutions requires the exploration of more efficient alternatives. Spiking neural networks (SNNs), inspired by the interconnections of human brain neurons, constitute a valid alternative to address this challenge. However, the advancement of these brain-inspired models is constrained by the limited accessibility to dedicated neuromorphic hardware capable of integrating brain-inspired computational principles. Despite this low hardware availability, SNNs represent a step forward in achieving more efficient, brain-inspired computation for edge devices and embedded systems.