In the last few years, there has been a real renaissance of Machine Learning. Neural Networks and especially Deep Neural Networks have shown broad applicability from object classification and detection, to speech recognition and natural language processing. The train and inference of an NNet are mainly executed on power-hungry systems like High-Performance CPU, clusters of CPUs and/or clusters of GPGPUs. The increased computational power of nowadays systems is a key element to understand the diffusion of Deep NNets. Deep Learning Algorithms are computationally intensive and require very large memory footprint, but there are multiple advantages in moving the computation at the edge, near the sensor.