The new paradigm of edge computing is expected to improve the agility of HPC platforms and cloud service deployments by utilizing opportunistic local computing resources. Implementing or analyzing new types of data-intensive applications close to where the data are generated is crucial for designing efficient distributed machine learning methods and, as a result, reducing data transfers, which consume the most energy.
The challenges are to design new machine learning methods that fully leverage the distributed nature of the edge and to develop algorithms and related software that will enable the deployment of hybrid edge/fog infrastructures.
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