Abstract
Edge computing is a paradigm in which data is intelligently processed close to its source. Along with advancements in deep learning, there is a growing interest in using deep neural networks at the edge for predictive analytics. Given the realistic constraints in computational resources of edge devices, this combination is challenging. In order to bridge the gap between deep learning models and efficient edge analytics, a container-based framework is presented that evaluates user-specified deep learning models for efficiency on the edge. The proposed framework is validated on a rotating machinery fault diagnosis use case. Conclusions on efficient state-of-the-art models for rotating machine fault diagnosis were drawn and appropriately reported.
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Notes
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The source code of the framework as well as the associated scripts to reproduce the performed experiments on the fault diagnosis use case are available via https://gitlab.com/Chandu1007/edge-benchmarking-framework.
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Sorescu, TG., Kancharla, C.R., Boydens, J., Hallez, H., Verbeke, M. (2023). Framework to Evaluate Deep Learning Algorithms for Edge Inference and Training. In: Koprinska, I., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol 1752. Springer, Cham. https://doi.org/10.1007/978-3-031-23618-1_38
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DOI: https://doi.org/10.1007/978-3-031-23618-1_38
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