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A Dynamic Deep Neural Network Design for Efficient Workload Allocation in Edge Computing

https://doi.org/10.1109/iccd.2017.49

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Gradient-based learning applied to document recognition

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ImageNet: A large-scale hierarchical image database

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Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

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Going deeper with convolutions

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Fully convolutional networks for semantic segmentation

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Deep Residual Learning for Image Recognition

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Resource optimization in fog enabled IoT deployments

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BranchyNet: Fast inference via early exiting from deep neural networks

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Optimal Workload Allocation in Fog-Cloud Computing Towards Balanced Delay and Power Consumption

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Caffe

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Edge-centric Computing

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On Estimation of a Probability Density Function and Mode

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Very Deep Convolutional Networks for Large-Scale Image Recognition

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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

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Wide Residual Networks

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What points at this work

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Dynamic Workload Allocation for Edge Computing

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Synergy of Edge Intelligence and Blockchain: A Comprehensive Survey

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