在多FPGA平台上实现自动驾驶汽车的生物灵感神经架构
Tarek Elouaret1,2, Sylvain Colomer1,2, Frédéric De Melo1
1Laboratoire ETIS, CY Cergy-Paris Université, ENSEA, CNRS, 95000 Cergy-Pontoise, France.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究介绍了一种节能硬件解决方案,用于使用现场可编程门阵列 (FPGA) 的自动驾驶汽车视觉定位. 与基于软件的方法相比,拟议的系统显著降低了延迟和功耗.
科学领域:
- 计算机工程 计算机工程
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 自动驾驶汽车依赖于精确的自我定位,相机是具有成本效益的传感器.
- 视觉定位要求高计算能力,实时处理和能源效率.
- 现场可编程门阵列 (FPGA) 为节能硬件加速提供了一个平台.
研究的目的:
- 提出和评估一个分布式的,基于硬件的解决方案,用于生物灵感的视觉定位模型.
- 评估FPGA实现对软件解决方案的能源效率和性能改进.
主要方法:
- 开发了一个图像处理知识产权 (IP) 核心用于地标检测.
- 在FPGA上实现了N-LOC生物灵感的神经架构.
- 为单个和多个FPGA平台创建了一个分布式N-LOC版本.
主要成果:
- 基于硬件的IP实现实现了高达9倍的延迟降低和7倍的吞吐量.
- 该系统表现出显著的能源效率,功率足迹低至2.741W.
- 功耗是比Nvidia Jetson TX2.2低5.5-6倍的功耗.
结论:
- 拟议的分布式FPGA解决方案为节能可视化定位提供了一个有希望的方法.
- 在FPGA上的硬件加速显著提高了性能,并降低了自动驾驶系统的功耗.
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