In this paper is presented a deep neural network architecture designed to run on a field-programmable gate array (FPGA) for detection vehicle on LIDAR point clouds. This works present a network based on VoxelNet adapted to run on an FPGA and to locate vehicles on point clouds from a 32 and a 64 channel optical sensor. For training the presented network the Kitti and Nuscenes dataset have been used. This work aims to motivate the usage of dedicated FPGA targets for training and validating neural network due to their accelerated computational capability compared to the well known GPUs. This platform also has some costraints that need to be assessed and taken care during development (limited memory e.g.). This research presents an implementation to overcome such limitations and obtain as good results as if a GPU would be used.

This paper makes use of a state-of-the-art dataset such us Nuscenes which is formed by several sensors and provides seven time more annotations than the KITTI dataset of the 6 cameras, 5 radars and 1 Lidar it is formed by, all with full 360 degree field of view. The presented work proves real-time performance and good detection accuracy when moving part of the CNN presented in the proposed architecture to a commercial FPGA.