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Titlebook: Robotic Computing on FPGAs; Shaoshan Liu,Zishen Wan,Yu Wang Book 2021 Springer Nature Switzerland AG 2021

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11#
發(fā)表于 2025-3-23 10:13:34 | 只看該作者
1935-3235 gns and summarizes their adopted optimized techniques. This book consists of ten chapters, delving into the details of how FPGAs have been utilized in robotic perception, localization, planning, and multi-robot collaboration tasks. In addition to individual robotic tasks, this book provides detailed
12#
發(fā)表于 2025-3-23 17:05:02 | 只看該作者
13#
發(fā)表于 2025-3-23 18:24:17 | 只看該作者
Autonomous Vehicles Powered by FPGAs,utonomous driving compute system, especially the choices between different compute units (mobile SoCs, GPUs, CPUs, FPGAs) and the utilization of FPGAs. Through this chapter, readers should be able to understand the importance of FPGAs in commercial robotic systems and the unique role FPGAs play in a heterogeneous compute system.
14#
發(fā)表于 2025-3-23 22:42:12 | 只看該作者
Book 2021erception, localization, planning, and multi-robot collaboration tasks. In addition to individual robotic tasks, this book provides detailed descriptions of how FPGAs have been used in robotic products, including commercial autonomous vehicles and space exploration robots.
15#
發(fā)表于 2025-3-24 04:27:41 | 只看該作者
Book 2021mmarizes their adopted optimized techniques. This book consists of ten chapters, delving into the details of how FPGAs have been utilized in robotic perception, localization, planning, and multi-robot collaboration tasks. In addition to individual robotic tasks, this book provides detailed descripti
16#
發(fā)表于 2025-3-24 08:58:32 | 只看該作者
Introduction and Overview, potential. Take drones, a type of aerial robot, as an example. The number of drones has grown by 2.83x between 2015 and 2019 based on the U.S. Federal Aviation Administration (FAA) report [8]. The registered number reached 1.32 million in 2019, and the FFA expects this number will grow to 1.59 billion by 2024.
17#
發(fā)表于 2025-3-24 11:45:07 | 只看該作者
18#
發(fā)表于 2025-3-24 17:28:25 | 只看該作者
,Perception on FPGAs — Deep Learning,nocular VO more robust and efficient. And monocular VO methods, like Depth-VO-Feat [70], make robot systems much easier to deploy than stereo ones. Furthermore, although there are previous works to design accelerators for robot applications, such as ESLAM [71], the accelerators can only be used for specific applications with poor scalability.
19#
發(fā)表于 2025-3-24 21:07:12 | 只看該作者
20#
發(fā)表于 2025-3-25 00:59:07 | 只看該作者
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