On a real 5G testbed, offloading a CNN with early exits and splits to a MEC server reduces inference delay by up to 2.5x and energy by up to 2.6x versus local processing, but the analytical models are fitted without independent validation.
Li, et al., Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing
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Real-World Modeling of Computation Offloading for Neural Networks with Early Exits and Splits
On a real 5G testbed, offloading a CNN with early exits and splits to a MEC server reduces inference delay by up to 2.5x and energy by up to 2.6x versus local processing, but the analytical models are fitted without independent validation.