Convolutional Neural Networks on Embedded Automotive Platforms: A Qualitative Comparison

Abstract

In the last decade, the rise of power-efficient, heterogeneous embedded platforms paved the way to the effective adoption of neural networks in several application domains. Especially, many-core accelerators (e.g., GPUs and FPGAs) are used to run Convolutional Neural Networks, e.g., in autonomous vehicles, and industry 4.0. At the same time, advanced research on neural networks is producing interesting results in computer vision applications, and NN packages for computer vision object detection and categorization such as YOLO, GoogleNet and AlexNet reached an unprecedented level of accuracy and performance. With this work, we aim at validating the effectiveness and efficiency of most recent networks on state-of-the-art embedded platforms, with commercial-off-the-shelf System-on-Chips such as the NVIDIA Tegra X2 and Xilinx Ultrascale+. In our vision, this work will support the choice of the most appropriate CNN package and computing system, and at the same time tries to "make some order" in the field.