An End-to-End Channel-Adaptive Feature Compression Approach in Device-Edge Co-Inference Systems

The emergence of various intelligent mobile applications necessitates the deployment of powerful deep learning models on resource-constrained devices. Device-edge co-inference offers a promising solution by allocating neural networks. It is necessary to balance the computation and communication cost by compressing intermediate features. Current methods for feature compression usually separate the inference task from communication design and have not yet considered the actual impact of wireless channels on feature compression. In this paper, we propose an end-to-end channel-adaptive feature compression approach to achieve efficient feature compression under wireless channels. Additionally, in order to fulfill human perception requirements, we propose a mirror model based on feature compression, aiming to restore images with the original resolution from compressed features. We conduct comprehensive experiments to validate the effectiveness of the proposed method.