Multi-Stage Semantic Communication for Low-Latency Edge Inference

Recently, deep learning (DL)-based semantic communication has emerged as a promising technique for enhancing transmission efficiency in various tasks. By transmitting the extracted semantic features instead of the original data, the communication delay can be significantly reduced. In this work, we design a semantic communication technique for low-latency device-edge co-inference systems which supports multi-stage semantic feature transmission. To reduce the average inference delay, we develop a multi-stage edge inference scheme and the corresponding early stopping strategy with the proposed semantic communication network. The edge inference would be directly terminated if the inference result at any stage is regarded as reliable. We also develop an algorithm to jointly optimize the transmission duration and decision threshold for both the two-stage and multi-stage inference schemes, aiming at minimizing the average inference delay while satisfying the inference accuracy requirement. Simulation results show that the proposed multi-stage edge inference scheme with early stopping strategy can effectively reduce the average edge inference delay.