Survey of machine learning on the edge
Survey of machine learning on the edge
A Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques
arXiv paper abstract https://arxiv.org/abs/2302.08571
arXiv PDF paper https://arxiv.org/ftp/arxiv/papers/2302/2302.08571.pdf
The union of Edge Computing (EC) and Artificial Intelligence (AI) has brought forward the Edge AI concept to provide intelligent solutions close to end-user environment, for privacy preservation, low latency to real-time performance, as well as resource optimization.
... edge powered ML solutions are more complex to realize due to the joint constraints from both edge computing and AI domains, and the corresponding solutions are expected to be efficient and adapted in technologies such as data processing, model compression, distributed inference, and advanced learning paradigms for Edge ML requirements.
... lack of a complete survey on existing Edge ML technologies to provide a common understanding of this concept.
To tackle this ... paper aims at providing a comprehensive taxonomy and a systematic review of Edge ML techniques ... start by identifying the Edge ML requirements driven by the joint constraints.
... then survey more than twenty paradigms and techniques along with their representative work, covering two main parts: edge inference, and edge learning.
... analyze how each technique fits into Edge ML by meeting a subset of the identified requirements ...
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