With the rise of the IoT paradigm (Internet of Things), the number of web-connected devices is increasing day by day, and with so, the interest in making use of the data they generate. Through the usage of data-driven models, that information can be leveraged to make decisions or perform forecasts providing solutions for a wide range of tasks, such as image classification, object detection, or speech recognition. Having affordable models requires a training procedure, a computationally expensive operation, which most traditionally has an open-form formulation, meaning that it can run for a virtually unlimited period. Although the training is usually performed in GPUs and TPUs, optimized hardware capable of efficiently handling MACs thanks to their parallelization, the energy required is often high, especially when the task of interest becomes harder, due to the need for more sophisticated architecture and/or a larger dataset, causing in turn, higher energy request that may have a relevant environmental impact.