Nowadays, Deep Learning represents the go-to-approach to solve recognition and prediction problems in a vast spectrum of application domains, including computer vision, time-series analysis, and natural language processing. For many of these tasks, shifting from a computing paradigm based on the cloud to an edge-centric one where models are deployed directly on IoT nodes provides several benefits, such as predictable response times and improved privacy. However, the execution of complex deep neural networks (DNN) on extreme-edge devices, such as low-power microcontrollers, is complicated by their tight constraints in terms of memory and energy consumption. Therefore, bringing "intelligence" at the IoT edge requires efficient architectures, that minimize the latency/energy consumption required for an inference, without sacrificing output quality (e.g., classification accuracy).