Comparative Survey of Embedded System Implementations of Convolutional Neural Networks in Autonomous Cars Applications - Mohammad Cheshfar; Mohammad Hossein Maghami; Parviz Amiri; Hossein Gharaee Garakani; Luciano Lavagno (IEEE Access, 2024)
In the rapidly evolving field of autonomous cars, advanced deep learning systems have ushered in a new era of innovation, enabling the integration of unique features into vehicles. These advancements span various areas, including pedestrian and vehicle detection, recognition of road signs and driving patterns, identification of drivable roads and scenes, and improved mapping and routing techniques. However, the high computational requirements of deep learning networks present a significant challenge, especially for embedded systems like FPGAs (Field-Programmable Gate Arrays) that have limited capacity. Addressing this challenge, this article presents a comprehensive survey of the methodologies employed in implementing Convolutional Neural Networks (CNNs) on resource-constrained processors, within the domain of self-driving car applications. Our survey encompasses a thorough review of the existing literature in the field of deep learning applied to autonomous cars, from perception to localization, with a specific emphasis on implementations utilizing embedded hardware such as FPGAs and embedded GPUs. Furthermore, we present and analyze results that elucidate the intricate trade-offs between latency, energy consumption, and the judicious selection of the underlying platform. These insights are crucial for researchers and practitioners in the field, as they provide a clear direction for optimizing the performance of deep learning networks on resource-constrained platforms, ultimately contributing to the advancement of self-driving car technologies.