In the last decade, the ever-increasing diffusion of machine learning algorithms for digital signal processing has drastically changed the hardware processing requirements for edge devices. These systems have to manage a large amount of data while often still responding to some events in real-time. To elaborate the information acquired by these systems, a suitable paradigm can be edge computing: instead of directly sending raw data to remote central servers, this can be partially processed close to where it is collected so that a smaller amount of elaborated data is sent to central systems, reducing the response time, the network-overloading and the overall power budget required.