Due to an increasing global interest towards sustainable and intelligent nano-chips and the need to sense the world at the extreme edge, a shift in microprocessor design is requested, since Von-Neumann architectures does not give considerable gain in term of speed and energy anymore. Considering also the end of the scaling era, disruptive approaches are needed in order to make systems which are power efficient and interacts with the world. Interaction has been empowered by use of Deep Learning, a paradigm of Artificial Intelligence (A.I.) in which an algorithm is trained by data in order to make inference, that is make rules and discover patterns.