FPGA Components for Integrating FPGAs into Robot Systems

Ohkawa, Takenao; Yamashina, Kazushi; Kimura, Hitomi; Ootsu, Kanemitsu; Yokota, Takashi

doi:10.1587/transinf.2017rcp0011

Cited by 17 publications

(9 citation statements)

References 18 publications

(20 reference statements)

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“…In addition, TCP/IP communication which is done by software on ARM processor is a large overhead. Although detailed analysis are done in our previous paper [9], the performance problem still remains as a problem.…”

Section: Resultsmentioning

confidence: 99%

“…ROS-compliant FPGA component [9] is a framework of hardware/software (HW/SW) for easy introduction of a processing circuit (application) operating on an FPGA to a robot system using ROS. Figure 2 shows a system model of ROS-compliant FPGA component.…”

Section: Ros-compliant Fpga Componentmentioning

confidence: 99%

“…We have proposed ROS-compliant FPGA component [9] in order to handle any FPGA processing circuit easily in robot systems. ROS (Robot Operating System) [4]- [6] is a kind of software platform that supports componentoriented development.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Generation Tool of FPGA Components for Robots

Ohkawa

Yamashina

Matsumoto

et al. 2019

IEICE Trans. Inf. & Syst.

Self Cite

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In order to realize intelligent robot system, it is required to process large amount of data input from complex and different kinds of sensors in a short time. FPGA is expected to improve process performance of robots due to better performance per power consumption than high performance CPU, but it has lower development productivity than software. In this paper, we discuss automatic generation of FPGA components for robots. A design tool, developed for easy integration of FPGA into robots, is proposed. The tool named cReComp can automatically convert circuit written in Verilog HDL into a software component compliant to a robot software framework ROS (Robot Operation System), which is the standard in robot development. To evaluate its productivity, we conducted a subject experiment. As a result, we confirmed that the automatic generation is effective to ease the development of FPGA components for robots.

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Section: Resultsmentioning

confidence: 99%

Section: Ros-compliant Fpga Componentmentioning

confidence: 99%

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Automatic Generation Tool of FPGA Components for Robots

Ohkawa

Yamashina

Matsumoto

et al. 2019

IEICE Trans. Inf. & Syst.

Self Cite

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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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“…• Localization [1150][1151][1152][1153][1154][1155][1156] • Trajectory/path planing [498,[1157][1158][1159][1160][1161][1162][1163] • Visual servoing [483,[1164][1165][1166][1167][1168][1169] • Navigation [11,1153,[1170][1171][1172][1173] • Stereo vision [1173][1174][1175][1176][1177] • Object/person follower/tracking [1168,[1178][1179][1180] • Ultrasonic Sensors [1181][1182][1183][1184][1185] • Robot Operating System (ROS) [1153,[1186][1187][1188][1189] • Educational [1190]…”

Section: Other Applicationsmentioning

confidence: 99%

Field Programmable Gate Array Applications—A Scientometric Review

2019

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Field Programmable Gate Array (FPGA) is a general purpose programmable logic device that can be configured by a customer after manufacturing to perform from a simple logic gate operations to complex systems on chip or even artificial intelligence systems. Scientific publications related to FPGA started in 1992 and, up to now, we found more than 70,000 documents in the two leading scientific databases (Scopus and Clarivative Web of Science). These publications show the vast range of applications based on FPGAs, from the new mechanism that enables the magnetic suspension system for the kilogram redefinition, to the Mars rovers’ navigation systems. This paper reviews the top FPGAs’ applications by a scientometric analysis in ScientoPy, covering publications related to FPGAs from 1992 to 2018. Here we found the top 150 applications that we divided into the following categories: digital control, communication interfaces, networking, computer security, cryptography techniques, machine learning, digital signal processing, image and video processing, big data, computer algorithms and other applications. Also, we present an evolution and trend analysis of the related applications.

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FPGA Components for Integrating FPGAs into Robot Systems

Cited by 17 publications

References 18 publications

Automatic Generation Tool of FPGA Components for Robots

Automatic Generation Tool of FPGA Components for Robots

RobotCore: An Open Architecture for Hardware Acceleration in ROS 2

Field Programmable Gate Array Applications—A Scientometric Review

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