研究CNN对异质边缘设备的协作推理机制
Jian Wang1, Chong Chen1, Shiwei Li1
1College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
边缘设备可以通过协作推理更快地运行复杂的卷积神经网络 (CNN). 这种方法将CNN任务划分到各种设备上,为资源有限的环境优化性能.
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:
- 卷积神经网络 (CNN) 对边缘计算中的智能传感器至关重要.
- 边缘设备上的有限资源和多样化的架构阻碍了本地CNN推断.
- 协作推断通过利用分布式资源和优化延迟来提供解决方案.
研究的目的:
- 在异质的,资源受限的边缘设备上实现CNN推理任务的高效协作执行.
- 为了应对高计算需求和有限的本地处理能力所带来的挑战.
主要方法:
- 对于CNN的分区前部署策略,确定任务划分的关键操作层.
- 使用数据压缩,排队和"微转移"技术优化管道并行性延迟.
主要成果:
- 在异质边缘环境中,CNN推断的显著加速.
- 与现有的流行的框架相比,性能提高了71.6%.
结论:
- 拟议的方法有效地克服了边缘设备上本地CNN推断的局限性.
- 通过战略分区和优化实现的协作推断是有效边缘AI的可行方法.
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