AirNet: Neural Network Transmission over the Air

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 must be delivered from an edge server to the edge device rapidly and efficiently to carry out time-sensitive inference tasks. In this paper, we introduce AirNet, a novel training and transmission method that allows efficient wireless delivery of DNNs under stringent transmit power and latency constraints. We first train the DNN with noise injection to counter the channel noise. We then employ pruning to reduce the network size to the available channel bandwidth, and perform knowledge distillation from a large model to improve the performance. We show that AirNet achieves significantly higher test accuracy compared to digital alternatives under the same bandwidth and power constraints. We further improve the performance of AirNet by pruning the network below the available bandwidth, and using channel expansion to provide better robustness against channel noise. We also benefit from unequal error protection (UEP) by selectively expanding more important layers of the network. Finally, we develop an ensemble training approach, which trains a whole spectrum of DNNs, each of which can be used at different channel condition, resolving the impractical memory requirements.