Optimum Selection of DNN Model and Framework for Edge Inference

Optimum Selection of DNN Model and Framework for Edge Inference

This paper describes a methodology to select the optimum combination of deep neural network and software framework for visual inference on embedded systems. As a first step, benchmarking is required. In particular, we have benchmarked six popular network models running on four deep learning framewor...

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Bibliographic Details
Main Authors: Delia Velasco-Montero, Jorge Fernandez-Berni, Ricardo Carmona-Galan, Angel Rodriguez-Vazquez
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Benchmarking
deep learning
convolutional neural networks
edge inference
embedded vision
high-level specifications
Online Access:https://ieeexplore.ieee.org/document/8463447/

Internet

https://ieeexplore.ieee.org/document/8463447/

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