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在边缘设备上调度多个深度神经网络 (DNN) 的层次调度器
Hyung Kook Jun1,2, Taeho Kim3, Sang Cheol Kim1
1Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Republic of Korea.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
本研究介绍了一种层次调度器,用于在边缘设备上调度深度神经网络 (DNN). 新的架构使异质处理单元 (PU) 的DNN执行效率提高了51.6%.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 嵌入式系统 嵌入式系统
背景情况:
- 边缘设备需要高效的深度神经网络 (DNN) 执行.
- 不同质的处理单元 (PU) 带来了调度挑战.
- 现有的解决方案缺乏适用于各种DNN工作负载的可扩展性.
研究的目的:
- 为边缘设备提供高效的DNN调度提出一个层次的调度器架构.
- 为了实现跨异质PU的可扩展和灵活的工作负载管理.
- 提高在边缘系统上部署的深度学习模型的性能.
主要方法:
- 开发了一个具有高层和低层组件的层次调度器.
- 将调度器功能与调度策略分开.
- 设计用于集成和分布式异质PU.
- 正式化了管理DNN子图的等级结构.
主要成果:
- 在案例研究中获得了51.6%的平均绩效改善.
- 在边缘设备上证明了实际适用性.
- 在同质和异质环境中展示了高效的工作负载管理.
- 为DNN部署提供了一个可扩展和适应的解决方案.
结论:
- 层次调度器架构有效地在异质边缘设备上安排DNN.
- 拟议的系统提供了显著的性能提升和适应性.
- 这种方法有助于在边缘计算中有效地部署深度学习模型.
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