Optimization of a Line Detection Algorithm for Autonomous Vehicles on a RISC-V with Accelerator

Abstract

In recent years, autonomous vehicles have attracted the attention of many research groups, both in academia and business, including researchers from leading com-panies such as Google, Uber and Tesla. This type of vehicles are equipped with systems that are subject to very strict requirements, essentially aimed at per-forming 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 par-ticular, the application is analyzed to identify the most computationally intensive parts of the code and it is adapted accordingly for more efficient processing. Fur-thermore, the code is executed on the aforementioned hardware platform to verify that the execution effec-tively meets the existing requirements in autonomous vehicles, experiencing a 3.7x speedup with respect to running without accelerator.