Due to a high contribution of human error in fatal traffic accidents, efforts in research and industry for automating vehicles steadily increased the last 3 decades. To reduce accidents with other road users, automated recognition and classification of road users is crucial. The most accurate models are large convolutional neural networks, however they require expensive and powerful hardware to be implemented. The hardware requirement hinders wide embedded use in education and industry, slowing down the potential increase of safety on the road. In pursue of reduced inference time on GPUs, research has focused to reduce the computational demand of neural networks. However the accelerations on microcomputers are not reported, potentially leading to different results.
To contribute to increased insight of the applicability of previous research on microcomputers, this thesis compares accelerations on a microcomputer and GPU. This is pursued by using the pretrained neural network "Squeezedet", trained on the "KITTI" road user dataset. The neural network is pruned in 3 different ways: by reducing the number of filters, by reducing the size of filter kernels along a layer and by reducing the amount of layers. The accelerations
are measured on a Raspberry Pi 3b+ and a Tesla K80 GPU. The process of embedding the neural network on the Raspberry Pi is described in great detail, to promote further research and educational use. Acceleration of the network is up to 12 % better on a Raspberry Pi than a GPU, when pruning filters from the network. When pruning full layers, or decreasing filter size the accelerations are up to 4% worse than on a GPU. This means when working with a microcomputer, the choice of pruning method can not be based on reported accelerations on GPUs. Therefore the recommendation is to do more research on accelerating neural networks for microcomputers. This will lead to wider use in industry and education and eventually safer roads.