YOLO is a system for detection and localization of objects in images using convolutional
neural networks. It is computationally intensive, so we want to use FPGAs for better performance. This master's thesis describes the implementation of the algorithm in C++ using high level synthesis. The speed of the algorithm is compared between software implementation on ARM processor and the FPGA. The effects of using different data types and sizes on computation are also explored. Using 24 bit fixed point numbers, that are optimal, on the small FPGA chip available on ZedBoard, we can achieve significantly faster implementation than a comparable processor system.