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23Chapter 2Deep Learning on Edge: Challenges and TrendsMrio P. Vistas https://orcid.org/0000000185564507 INES CID, ISEG, Institute Politico de Lisboa, PortugalABSTRACT Deep learning on edge has been
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Deep learning on edge refers to the implementation of deep learning algorithms and models directly on edge devices, such as smartphones, IoT devices, and other localized hardware, enabling real-time data processing and decision-making without relying on cloud resources.
Entities or individuals who utilize deep learning technologies on edge devices in their operations may be required to file relevant documentation, typically if there are compliance or regulatory requirements specific to their industry.
Filling out deep learning on edge typically involves gathering necessary information about the models used, the data processed, and how the edge devices operate, and then completing the relevant forms or reports as mandated by authorities.
The purpose of deep learning on edge is to improve response times and operational efficiency by processing data locally, reducing latency, and minimizing the need for bandwidth by avoiding reliance on cloud computing.
Information that must be reported can include details about the algorithms employed, types of data being processed, outcomes of the processing, and any compliance with relevant regulations or standards.
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