Nowadays image recognition algorithms are used in various fields, which go from simple mobile phone face recognition, to detect object from drones but also to land rovers on Mars. Among these algorithms, the Convolution Neural Networks (CNN) are the most used one. Even if their construction and structure is very simple and easy to be understood, their computational cost and memory requirements are nowadays challenging, especially when the network is inferred on FPGAs, which are the most suitable devices for embedded systems and data-centers, due to the low energy consumption. In this thesis work an architecturally optimized CNN is considered as starting point for further data precision optimization.