Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels

Understand

Today's intelligent applications can achieve high performance accuracy using machine learning (ML) techniques, such as deep neural networks (DNNs).

• Traditionally, in a remote DNN inference problem, an edge device transmits raw data to a remote node that performs the inference task.
• However, this may incur high transmission energy costs and puts data privacy at risk.
• In this paper, we propose a technique to reduce the total energy bill at the edge device by utilizing model compression and time-varying model split between the edge and remote nodes.