Nowadays, Artificial Intelligence (AI), especially in the form of Machine Learning (ML) and Deep Learning (DL), is becoming the go-to approach to solve complex problems in several sectors such as Computer Vision (CV), Speech Recognition, Natural Language Processing (NLP) and many others. Despite the huge effort spent by public and private actors to reach state-of-the-art results, the design of Deep Neural Networks (DNNs) is still a manual process heavily based on empirical rules and heuristics, thus requiring designers with strong expertise. This inspired researchers to define a new set of algorithms called Neural Architecture Search (NAS). NAS algorithms are becoming very popular in the TinyML/TinyDL domain where the choice of the specific ML/DL model structure is of primal importance.