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

• Main Authors: Belda, María José, Olcoz, Katzalin, Castro, Fernando, Tirado, Francisco
• Format: Journal Article
• Language: English
• Published: 01-02-2024
• Subjects: Computer Science - Hardware Architecture
• DOI: 10.48550/arxiv.2402.00496

Journal of Computer Science & Technology, 22(2), 2022 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.