Automated Integration of High-Level Synthesis FPGA Modules with ROS2 Systems

Leal, Daniel Pinheiro; Sugaya, Midori; Amano, Hideharu; Ohkawa, Takenao

doi:10.1109/icfpt51103.2020.00052

Cited by 10 publications

(3 citation statements)

References 2 publications

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“…With ROS2, specific applications have been demonstrated such as hardware accelerated reinforcement learning [13] and object detection [14], with clear performance gains compared to CPU implementations. Additionally, a tool has been presented in [15] for automatic generation of ROS2 nodes for integration with FPGA modules built with the Vivado HLS tool, a predecessor to Vitis HLS. Additionally, methodologies have been proposed for general integration of FPGAs with ROS [16] [17], and for full or partial implementation of ROS2 nodes on FPGAs [18].…”

Section: Related Workmentioning

confidence: 99%

MPSoC4Drones: An Open Framework for ROS2, PX4, and FPGA Integration

Nyboe

Malle

Ebeid

2022

2022 International Conference on Unmanned Aircraft Systems (ICUAS)

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Autonomous drones are facing a tough efficiency challenge due to limitations on the utilized processing hardware units. Among these limitations is the tradeoff between fast computing and low power consumption; between functional complexity and flight time. Recent progressions point to FPGAs for accelerating heavy processing. In this work, we present the MPSoC4Drones Framework; a novel framework for organizing FPGA-design and OS build projects. The framework combines tools for creating bootable images for the Ultra96-V2 board.We show how MPSoC4Drones organizes the build, combining the latest well-known tools for research and industrial drone development, Ubuntu 20.04, PX4 autopilot, and ROS2 middleware. We validate the framework through a computationally intensive deep learning use case implemented in the MPSoC4Drones framework. We show the superior throughput and low power consumption of FPGA processing compared to classical CPU and GPU approaches. Finally, we offer the full framework as open-source.

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Section: Related Workmentioning

confidence: 99%

MPSoC4Drones: An Open Framework for ROS2, PX4, and FPGA Integration

Nyboe

Malle

Ebeid

2022

2022 International Conference on Unmanned Aircraft Systems (ICUAS)

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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%

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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“…There has been previous work that has focused on ways to accelerate robotics applications by developing tools and methodologies to help roboticists leverage hardware acceleration for selected ROS Nodes and to optimize the ROS computational graph through adaptive computing [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [24]. There has also been some work to accelerate the scheduling and communication layers used by ROS and ROS 2 [44], [45], [46], [47], [48], [49], [50], [51].…”

Section: B Hardware Acceleration For Ros and Rosmentioning

confidence: 99%

RobotCore: An Open Architecture for Hardware Acceleration in ROS 2

Mayoral-Vilches¹,

Neuman²,

Plancher³

et al. 2022

Preprint

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Hardware acceleration can revolutionize robotics, enabling new applications by speeding up robot response times while remaining power-efficient. However, the diversity of acceleration options makes it difficult for roboticists to easily deploy accelerated systems without expertise in each specific hardware platform. In this work, we address this challenge with RobotCore, an architecture to integrate hardware acceleration in the widely-used ROS 2 robotics software framework. This architecture is target-agnostic (supports edge, workstation, data center, or cloud targets) and accelerator-agnostic (supports both FPGAs and GPUs). It builds on top of the common ROS 2 build system and tools and is easily portable across different research and commercial solutions through a new firmware layer. We also leverage the Linux Tracing Toolkit next generation (LTTng) for low-overhead real-time tracing and benchmarking. To demonstrate the acceleration enabled by this architecture, we use it to deploy a ROS 2 perception computational graph on a CPU and FPGA.We employ our integrated tracing and benchmarking to analyze bottlenecks, uncovering insights that guide us to improve FPGA communication efficiency. In particular, we design an intra-FPGA ROS 2 node communication queue to enable faster data flows, and use it in conjunction with FPGA-accelerated nodes to achieve a 24.42% speedup over a CPU.

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Automated Integration of High-Level Synthesis FPGA Modules with ROS2 Systems

Cited by 10 publications

References 2 publications

MPSoC4Drones: An Open Framework for ROS2, PX4, and FPGA Integration

MPSoC4Drones: An Open Framework for ROS2, PX4, and FPGA Integration

RobotCore: An Open Architecture for Hardware Acceleration in ROS 2

RobotCore: An Open Architecture for Hardware Acceleration in ROS 2

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