RoSÉ: A Hardware-Software Co-Simulation Infrastructure Enabling Pre-Silicon Full-Stack Robotics SoC Evaluation

Nikiforov, Dmitry; Dong, Shuilong; Zhang, C; Kim, Seah; Nikolić, Borivoje; Shao, Yakun Sophia

doi:10.1145/3579371.3589099

Cited by 9 publications

(2 citation statements)

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“…Finally, previous work has leveraged hardware acceleration for select ROS Nodes and adaptive computing to optimize the ROS computational graphs [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79]. However, these works do not provide comprehensive frameworks to quickly analyze and evaluate new heterogeneous computational graphs except for two works that are limited to the context of UAVs [25], [28].…”

Section: B Robotics Benchmarksmentioning

confidence: 99%

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RobotCore: An Open Architecture for Hardware Acceleration in ROS 2

Mayoral-Vilches¹,

Neuman

Plancher

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We introduce RobotPerf, a vendor-agnostic benchmarking suite designed to evaluate robotics computing performance across a diverse range of hardware platforms using ROS 2 as its common baseline. The suite encompasses ROS 2 packages covering the full robotics pipeline and integrates two distinct benchmarking approaches: black-box testing, which measures performance by eliminating upper layers and replacing them with a test application, and grey-box testing, an application-specific measure that observes internal system states with minimal interference. Our benchmarking framework provides ready-to-use tools and is easily adaptable for the assessment of custom ROS 2 computational graphs. Drawing from the knowledge of leading robot architects and system architecture experts, RobotPerf establishes a standardized approach to robotics benchmarking. As an open-source initiative, RobotPerf remains committed to evolving with community input to advance the future of hardware-accelerated robotics.

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Section: B Robotics Benchmarksmentioning

confidence: 99%

“…[20], [21], [22], and while benchmarks for specific robotics algorithms [23], [24] and certain end-to-end robotic applications, such as drones [25], [26], [27], [28], do exist, the nuances of analyzing general ROS 2 computational graphs on heterogeneous hardware is yet to be fully understood.…”

Section: Introductionmentioning

confidence: 99%