State-of-the-art performance for many edge applications is achieved by deep neural networks (DNNs). Often, these DNNs are location- and time-sensitive, and must be delivered over a wireless channel rapidly and efficiently. In this paper, we introduce AirNet, a family of novel training and transmission methods that allow DNNs to be efficiently delivered over wireless channels under stringent transmit power and latency constraints. This corresponds to a new class of joint source-channel coding problems, aimed at delivering DNNs with the goal of maximizing their accuracy at the receiver, rather than recovering them with high fidelity. In AirNet, we propose the direct mapping of the DNN parameters to transmitted channel symbols, while the network is trained to meet the channel constraints, and exhibit robustness against channel noise. AirNet achieves higher accuracy compared to separation-based alternatives. We further improve the performance of AirNet by pruning the network below the available bandwidth, and expanding it for improved robustness. We also benefit from unequal error protection by selectively expanding important layers of the network. Finally, we develop an approach, which simultaneously trains a spectrum of DNNs, each targeting a different channel condition, resolving the impractical memory requirements of training distinct networks for different channel conditions.

翻译：最先进的深度神经网络（DNN）为众多边缘应用实现了卓越性能。这些DNN通常具有位置敏感性和时间敏感性，需通过无线信道快速高效地传输。本文提出AirNet系列新型训练与传输方法，使DNN能在严格的发射功率和延迟约束下通过无线信道高效传输。这对应一类新的联合信源信道编码问题——其目标并非高保真地恢复DNN，而是在接收端最大化其准确率。AirNet提出将DNN参数直接映射为传输信道符号，同时通过网络训练满足信道约束，并增强对信道噪声的鲁棒性。相较于分离编码方案，AirNet能实现更高的准确率。我们进一步通过将网络剪枝至可用带宽以下、再扩展网络来提升鲁棒性，从而优化AirNet性能；还通过对网络关键层进行选择性扩展实现不等差错保护。最后，我们开发了一种能同时训练面向不同信道条件的DNN谱系的方法，解决了为不同信道条件分别训练独立网络时存在的存储需求不切实际的问题。