In recent years, autonomous vehicles have attracted the attention of many research groups, both in academia and business, including researchers from leading companies such as Google, Uber and Tesla. This type of vehicles are equipped with systems that are subject to very strict requirements, essentially aimed at performing safe operations -- both for potential passengers and pedestrians -- as well as carrying out the processing needed for decision making in real time. In many instances, general-purpose processors alone cannot ensure that these safety, reliability and real-time requirements are met, so it is common to implement heterogeneous systems by including accelerators. This paper explores the acceleration of a line detection application in the autonomous car environment using a heterogeneous system consisting of a general-purpose RISC-V core and a domain-specific accelerator. In particular, the application is analyzed to identify the most computationally intensive parts of the code and it is adapted accordingly for more efficient processing. Furthermore, the code is executed on the aforementioned hardware platform to verify that the execution effectively meets the existing requirements in autonomous vehicles, experiencing a 3.7x speedup with respect to running without accelerator.

翻译：近年来，自动驾驶车辆吸引了学术界和商业界众多研究团队的关注，包括来自谷歌、优步和特斯拉等领先企业的研究人员。这类车辆配备的系统需满足极为严格的要求，其核心目标包括确保安全运行（无论是对潜在乘客还是行人），以及实时完成决策所需的处理任务。在许多情况下，仅凭通用处理器无法满足这些安全性、可靠性和实时性要求，因此通常通过集成加速器来实现异构系统。本文探索了在自动驾驶环境中，利用由通用RISC-V核心与领域专用加速器构成的异构系统加速线条检测应用的方法。具体而言，通过分析应用以识别代码中计算最密集的部分，并对其进行相应调整以实现更高效的处理。此外，在所述硬件平台上执行代码以验证其实际满足自动驾驶车辆的现有要求，相较于未使用加速器的运行实现了3.7倍的加速比。