Over the last decade, standard computing architectures based on von Neumann paradigm struggled to manage Internet of Things and Artificial Intelligence (AI) workloads. The inherently inefficient data transfer between processing and storage units is not suited to deal with modern data-centric applications, which are growing in complexity and scale. To tackle the exponentially increasing power demand of Neural Networks computing tasks, new low-power hardware implementations are necessary. In-Memory computing has the potential to fulfill the energy requirements in the modern Information Technology field, enabling parallel data processing and reducing latency. Memristive devices within cross-point architectures turned out to be a promising solution to perform analog In-Memory Computing, allowing to map multi-level weights between Neural Networks layers.