Convolutional Neural Network implemented on FPGA for trajectory classification

Artificial intelligence (AI) models are popular nowadays due to their capacity to provide accurate classification results. A hardware implementation of an AI model offers major benefits such as deterministic and predictable functioning, low power consumption and the possibility of embedding the AI model in a mobile application. In this paper we present the hardware implementation of a Convolutional Neural Network (CNN) on a Xilinx Field Programmable Gate Array (FPGA) device. Our implementation is compact (small area) being aimed at very low power consumption. We describe the architecture and the choices we made for the parallel implementation. We prove the usefulness of our approach for simple trajectory classification. We conclude that our hardware implementation leads to interesting experimental results in terms of resource utilization, power consumption and running speed.