Model-based Generation of Hardware/Software Architectures for Robotics Systems

Podlubne, Ariel; Mey, Johannes; Pertuz, Sergio; Aßmann, Uwe; Göhringer, Diana

doi:10.1109/fpl57034.2022.00034

Cited by 1 publication

(1 citation statement)

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“…This work builds upon our previous one [10], extending the pool of scheduling algorithms and incorporating Domain Space Exploration (DSE) for evaluation and selection of the most suitable algorithm for each application. To the best of the authors' knowledge, no comparison of multiple HW IP-based scheduling algorithms, including the one presented in this work, has been conducted in the given context.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Generation of Hardware/Software Architectures With Hybrid Schedulers for Robotics Systems

Podlubne,

Mey,

Andreou

et al. 2024

IEEE Trans. Comput.

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Robotic systems compute data from multiple sensors to perform several actions (e.g., path planning, object detection). FPGA-based architectures for such systems may consist of several accelerators to process compute-intensive algorithms. Designing and implementing such complex systems tends to be an arduous task. This work proposes a modeling approach to generate architectures for such applications, compliant with existing robotics middlewares (e.g., ROS, ROS2). The challenge is to have a compact, yet expressive description of the system with just enough information to generate all required components and to integrate existing algorithms. The system model must be application-independent and leverage FPGA advantages, such as concurrency, energy efficiency, and acceleration due to custom designs, surpassing software-based solutions. Previous work mainly focused on individual accelerators rather than all components involved in a system and their interactions. The proposed approach exploits the advantages of model-driven engineering and model-based code generation to produce all components, i.e., message converters as middleware interfaces and wrappers to integrate algorithms. Data type and data flow analysis are performed to derive the necessary information to generate the components and their connections. Six different schedulers are proposed to cover multiple scenarios. Solutions to several identified challenges for generating entire systems from such models are evaluated using four different use cases.

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Section: Introductionmentioning

confidence: 99%