Optimization of DNN Inference Offloading via Computation-Aware and Semantic Communication Techniques in MEC

Abstract

This paper examines the way phones and IoT devices run deep neural network (DNN) inference with help from a nearby edge server. It uses semantic communication (SC) to shrink the data sent over the air. The work notes that local computing power is limited. It connects two aspects: the level of data compression and the amount of local computation needed to achieve that compression. This link is modeled as a piecewise linear function across DNN layers. Early layers compute less and compress less. Deeper layers compute more and compress more. The objective is to minimize total user energy. The threshold depends on the radio channel quality and the local energy cost per compute cycle. A user above the threshold should upload earlier with less compression. A user below the threshold should compute more locally to compress the data. Next, a low-complexity iterative algorithm is designed for the general case. It iteratively updates the split layer and resources. The proposed scheme saves much more energy than full offloading and then conventional partial offloading. The gains are largest when wireless bandwidth is scarce. The approach scales to many users and suits real mobile edge scenarios. The model is transparent. Assumptions suit edge networks well. It connects theory, algorithms and experiments into one unified framework for energy minimization tasks.