Fully distributed deep learning inference on resource-constrained edge devices
Performing inference tasks of deep learning applications on IoT edge devices ensures
privacy of input data and can result in shorter latency when compared to a cloud solution. As …
privacy of input data and can result in shorter latency when compared to a cloud solution. As …
[PDF][PDF] Fully Distributed Deep Learning Inference on Resource-Constrained Edge Devices
R Stahl, Z Zhao, D Mueller-Gritschneder, A Gerstlauer… - zoranzhao.github.io
Performing inference tasks of deep learning applications on IoT edge devices ensures
privacy of input data and can result in shorter latency when compared to a cloud solution. As …
privacy of input data and can result in shorter latency when compared to a cloud solution. As …
Fully Distributed Deep Learning Inference on Resource-Constrained Edge Devices
A Gerstlauer, U Schlichtmann - Embedded Computer Systems: Architectures … - Springer
Performing inference tasks of deep learning applications on IoT edge devices ensures
privacy of input data and can result in shorter latency when compared to a cloud solution. As …
privacy of input data and can result in shorter latency when compared to a cloud solution. As …
Fully Distributed Deep Learning Inference on Resource-Constrained Edge Devices
A Gerstlauer, U Schlichtmann - … 2019, Samos, Greece, July 7–11 …, 2019 - books.google.com
Performing inference tasks of deep learning applications on IoT edge devices ensures
privacy of input data and can result in shorter latency when compared to a cloud solution. As …
privacy of input data and can result in shorter latency when compared to a cloud solution. As …
Fully Distributed Deep Learning Inference on Resource-Constrained Edge Devices
R Stahl, Z Zhao, D Mueller-Gritschneder… - International …, 2019 - dl.acm.org
Performing inference tasks of deep learning applications on IoT edge devices ensures
privacy of input data and can result in shorter latency when compared to a cloud solution. As …
privacy of input data and can result in shorter latency when compared to a cloud solution. As …
[引用][C] Fully Distributed Deep Learning Inference on Resource-Constrained Edge Devices
R Stahl, Z Zhao, D Mueller-Gritschneder… - International …, 2019 - mediatum.ub.tum.de
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[PDF][PDF] Fully Distributed Deep Learning Inference on Resource-Constrained Edge Devices
R Stahl, Z Zhao, D Mueller-Gritschneder, A Gerstlauer… - slam.ece.utexas.edu
Performing inference tasks of deep learning applications on IoT edge devices ensures
privacy of input data and can result in shorter latency when compared to a cloud solution. As …
privacy of input data and can result in shorter latency when compared to a cloud solution. As …