Ultra-Low-Latency Robust Edge Inference: An Integrated Computation and Communication Design

Abstract

The advent of sixth-generation (6G) mobile networks forecasts the widespread deployment of edge artificial intelligence (AI), where AI inference tasks are offloaded from resource-constrained edge devices to edge servers. This paradigm promises low latency and high-efficiency processing, yet faces critical challenges in meeting the stringent latency requirements of emerging applications such as autonomous driving and real-time robotics. Traditional ultra-reliable and low-latency communication (URLLC) paradigms fall short in the context of edge inference, where the high-dimensional nature of extracted features introduces a fundamental trade-off between reliability and latency. In this paper, we exploit the inherent robustness of AI models to channel distortions to design an ultra-low-latency edge inference framework that jointly optimizes computation and communication resources. We focus on both multi-snapshot (sequential sensing) and multi-view (distributed sensing) scenarios. For each, we derive upper bounds on inference accuracy as a function of the number of snapshots/views and their average bit error rate (BER). These bounds guide the formulation of optimization problems that minimize total system latency while satisfying accuracy constraints. The joint optimization is decomposed into subproblems involving snapshot/view selection, transmit power control, and allocation of computation and communication time. To solve this efficiently, we develop a low-complexity iterative algorithm. Experimental results on synthetic and real-world datasets validate our approach, demonstrating significant latency reductions while maintaining inference performance. Our findings provide a foundation for robust and efficient edge AI design in future 6G networks.