Energy-Efficient Optimal Mode Selection for Edge AI Inference via Integrated Sensing-Communication-Computation

Existing edge inference methods only consider one paradigm, i.e., one of on-device inference, on-server inference, or edge-device cooperative inference. Each paradigm has its pros and cons as well as dominant application scopes. For example, the on-device paradigm is the best choice when the inference task is not computationally intensive, the on-server paradigm is suitable if the communication capacity is strong, and the edge-device cooperative mode should be selected in the scenario of weak on-device communication and computation. However, each paradigm suffers from poor performance if deployed outside of its application scope, thus leading to limited potential and flexibility. This paper proposes an edge AI inference framework, which makes the first attempt to jointly consider the three modes for making full use of their benefits. In addition, sensing for data acquisition is enabled at both the edge server and the device. This can effectively improve the inference accuracy with rich information on the target area from two different views. On the other hand, energy cost minimization turns out to be a key target all over the world and a significant issue in wireless networks. To this end, we target minimizing the system energy cost under a given inference accuracy guarantee and other network resource constraints, by coordinating sensing, communication, and computation in different modes. By optimally solving the optimization problem, an integrated sensing-communication-computation (ISCC) based task-oriented mode selection scheme is proposed. A practical ISCC platform is built and extensive experiments are conducted to verify our theoretical analysis.

I. Introduction

Integrated artificial intelligence (AI) and communication has been recognized as one of the six usage scenarios of 6G by IMT-2030 [1]. One fundamental function of this usage scenario is providing ubiquitous intelligent services at the network edge [2], [3], [4], [5]. To implement these intelligent services, it is desirable to deploy well-trained AI models at the network edge, giving rise to a research area called edge AI inference or edge inference [6], [7], [8].