Despite the attractive qualities of convolutional neural networks (CNNs), and the universality of architectures emerging now, CNNs are still prohibitive regarding environmental impact due to electric consumption or carbon footprint, as well as deployment in constrained platform such as microcomputers. We address this problem and sketch how PAC-Bayesian theory can be applied to learn lighter