State-of-the-art performance for many emerging edge applications is achieved by deep neural networks (DNNs). Often, these DNNs are location and time sensitive, and the parameters of a specific DNN must be delivered from an edge server to the edge device rapidly and efficiently to carry out time-sensitive inference tasks. We introduce AirNet, a novel training and analog transmission method that allows efficient wireless delivery of DNNs. We first train the DNN with noise injection to counter the wireless channel noise. We also employ pruning to reduce the channel bandwidth necessary for transmission, and perform knowledge distillation from a larger model to achieve satisfactory performance, despite the channel perturbations. We show that AirNet achieves significantly higher test accuracy compared to digital alternatives under the same bandwidth and power constraints. It also exhibits graceful degradation with channel quality, which reduces the requirement for accurate channel estimation.
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