Today machine learning techniques and, in particular, deep neural networks are widely used for multiple tasks. Convolutional neural networks improve the performance of artificial intelligence algorithms in many applications (e.g. image recognition, object detection, natural language processing) but result to be energy-intensive due to the large amount of multiplications involved. This type of model is unsuitable for IoT devices or edge devices (such as smartphones) which are limited in terms of memory and computational capacity. For this reason, many studies are focusing on developing more efficient deep network architectures. This thesis analyses two modern techniques: depthwise separable convolution and Addernet.The first allows designing deep networks with fewer parameters while the second reduces the usage of hardware resources and decreases the computational and energy cost by substituting multiplications with additions.