Energy Aware Collaborative Machine Learning on Energy-Harvesting Devices

Performing machine learning tasks on low end devices enables the development of various smart applications. Especially, these low end devices are often equipped with ultra-low-power microcontroller units (MCUs) that have weak computation power and few memory resources. It is a more challenging work...

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Bibliographic Details
Main Authors: Sun, QiHui, Tu, Chia-Heng
Format: Conference Proceeding
Language:English
Subjects:
Adaptive systems
Collaboration
Energy consumption
Energy Harvesting
IoT
Machine learning
Microcontrollers
Performance evaluation
Schedules
self-powered system
TinyML
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Summary:Performing machine learning tasks on low end devices enables the development of various smart applications. Especially, these low end devices are often equipped with ultra-low-power microcontroller units (MCUs) that have weak computation power and few memory resources. It is a more challenging work to put these machine learning tasks on those end devices powered by harvested ambient energy, which are often referred to as energy-harvesting (EH) devices, since the unstable ambient energy can lead to the execution failure of the machine learning tasks. This paper proposes an adaptive energy-aware design to coordinate multiple EH devices to accomplish multi-class classification computation. It also leverages the concept of the One-vs-All (OVA) strategy turning a multi-class classification into multiple binary classifications. The experimental results show our work performs better than the widely used round-robin policy and self-greedy policy in consideration of time and energy consumption.
ISSN:2575-8284
DOI:10.1109/ICCE-Taiwan58799.2023.10227007