对于计算机视觉的GPU加速边缘计算单板计算机 (SBC) 的最新硬件和软件进展的审查
Umair Iqbal1, Tim Davies1, Pascal Perez2
1SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW 2522, Australia.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
这篇评论探讨了GPU加速单板计算机 (SBC) 和边缘计算软件. 它详细介绍了SBC计算机视觉 (CV) 的进展,解决了智能城市应用的挑战.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 边缘计算 边缘计算
背景情况:
- 计算机视觉 (CV) 对单板计算机 (SBC) 在智能城市应用中至关重要.
- 在SBC上部署CV面临诸如有限的计算能力,能源效率和实时处理等挑战.
- 现有的研究重点是GPU加速和SBC性能的软件进步.
研究的目的:
- 为GPU加速边缘计算SBC和软件进步提供全面的审查.
- 分析最近在算法优化,SBCs上的CV包和框架中的最新进展.
- 引导人工智能研究人员为他们的用例选择最佳的硬件-软件组合.
主要方法:
- 对 GPU 加速 SBC 边缘计算的近期进展的文献综述.
- 详细概述软件开发,包括算法优化和部署包.
- 基于关键绩效因素对SBC进行主观比较分析.
主要成果:
- 确定了关键的GPU加速SBC和边缘AI的软件解决方案.
- 详细介绍了各种算法优化技术和开发框架.
- 提供了比较分析,以帮助选择合适的SBC和软件.
结论:
- GPU加速和软件优化对于在SBC上推进CV至关重要.
- 需要进一步的研究来解决边缘AI当前SBC的局限性.
- 该审查提供了对SBCsCV的最新情况和未来方向的见解.
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