Time-efficient offloading for machine learning tasks between embedded systems and fog nodes

Darren Saguil, Akramul Azim
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引用次数: 7

Abstract

The Internet of Things (IoT) and Machine Learning (ML) introduce embedded systems to many new roles and functions, but the current status quo of using these technologies together can be improved. The status quo has embedded systems offloading all of their ML functionality to an external device, but this can lead to unpredictable throughput due to network instability. We propose to run low-complexity ML models on the embedded system itself and distribute the workload when it has been measured to bypass a Worst-Case Execution Time (WCET) threshold.
在嵌入式系统和雾节点之间实现高效的机器学习任务卸载
物联网(IoT)和机器学习(ML)为嵌入式系统引入了许多新的角色和功能,但目前将这些技术结合使用的现状可以得到改善。目前的现状是嵌入式系统将其所有机器学习功能卸载到外部设备,但由于网络不稳定,这可能导致不可预测的吞吐量。我们建议在嵌入式系统本身上运行低复杂性的ML模型,并在测量工作负载以绕过最坏情况执行时间(WCET)阈值时分配工作负载。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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