Distributed Collaborative Inference System in Next-Generation Networks and Communication

TL;DR

This paper proposes a multi-level collaborative inference system for 6G networks that reduces latency and improves efficiency in generative AI tasks by deploying models across network layers and optimizing task offloading.

Contribution

It introduces a novel deployment and task offloading strategy combined with an early exit mechanism for efficient GAI inference in next-generation networks.

Findings

Reduces inference latency by up to 17%

Maintains high inference accuracy

Enhances efficiency in resource-constrained devices

Abstract

With the rapid advancement of artificial intelligence, generative artificial intelligence (GAI) has taken a leading role in transforming data processing methods. However, the high computational demands of GAI present challenges for devices with limited resources. As we move towards the sixth generation of mobile networks (6G), the higher data rates and improved energy efficiency of 6G create a need for more efficient data processing in GAI. Traditional GAI, however, shows its limitations in meeting these demands. To address these challenges, we introduce a multi-level collaborative inference system designed for next-generation networks and communication. Our proposed system features a deployment strategy that assigns models of varying sizes to devices at different network layers. Then, we design a task offloading strategy to optimise both efficiency and latency. Furthermore, a modified early exit mechanism is implemented to enhance the inference process for single models. Experimental results demonstrate that our system effectively reduces inference latency while maintaining high-quality output. Specifically, compared to existing work, our system can reduce inference time by up to 17% without sacrificing the inference accuracy.