Technologies for distributing gradient descent computation in a heterogeneous multi-access edge computing (MEC) networks
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United States of America Patent
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Feb 8, 2022
Grant Date -
May 9, 2019
app pub date -
Dec 28, 2018
filing date -
Sep 7, 2018
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Abstract
Systems, apparatuses, methods, and computer-readable media, are provided for distributed machine learning (ML) training using heterogeneous compute nodes in a heterogeneous computing environment, where the heterogeneous compute nodes are connected to a master node via respective wireless links. ML computations are performed by individual heterogeneous compute nodes on respective training datasets, and a master combines the outputs of the ML computations obtained from individual heterogeneous compute nodes. The ML computations are balanced across the heterogeneous compute nodes based on knowledge of network conditions and operational constraints experienced by the heterogeneous compute nodes. Other embodiments may be described and/or claimed.
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