The advancement of silicon technology is revolutionizing the world in terms of processing power. Algorithms and complex mathematical models that require much computing have been made feasible with ease in the last decade. One of such booming algorithms is Convolutional Neural Network (CNN), a field of Artificial Intelligence that can make very complex predictions. However, with high computing complexity comes the drawback of high power consumption for processing, which is a significant concern for some applications. This thesis describes the Field Programmable Gate Array (FPGA) implementation of a CNN algorithm used to predict the location of people indoors using infrared sensors data, an application that benefits from the high prediction power of the CNN but requires a low power implementation of it.