Characterizing self-driving tasks in general-purpose architectures
Document typePart of book or chapter of book
PublisherEuropean Network of Excellence on High Performance and Embedded Architecture and Compilation (HiPEAC)
Rights accessOpen Access
European Commission's projectCoCoUnit - CoCoUnit: An Energy-Efficient Processing Unit for Cognitive Computing (EC-H2020-833057)
Autonomous Vehicles (AVs) have the potential to radically change the automotive industry. How- ever, computing solutions for AVs have to meet severe performance constraints to guarantee a safe driving experience. Current solutions either exhibit high cost or fail to meet the stringent latency constraints. Therefore, the popularization of AVs requires a low-cost yet effective computing sys- tem. Understanding the sources of latency is key in order to improve autonomous driving systems. Here, we present a detailed characterization of Autoware, a modern self-driving car system. We analyze the performance of the different components and leverage hardware counters to identify the main bottlenecks.
CitationExenberger, P.; Arnau, J.; González, A. Characterizing self-driving tasks in general-purpose architectures. A: "ACACES 2021 poster abstracts: September 15, 2021 Fiuggi, Italy". European Network of Excellence on High Performance and Embedded Architecture and Compilation (HiPEAC), 2021, p. 117-120.
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