A Unified Framework for Task-Oriented Communication at Edge AI

Adapting task-oriented communication to the distributed edge artificial intelligence (AI) is a promising paradigm. On the other hand, the proliferation of diverse intelligent applications in edge AI makes employing a specialized, dedicated model for each task infeasible. This paper studies a unified framework that enables loading a single scalable network/tensor graph into memory for multi-task edge AI. The key features of the developed architecture are the injection of parallelism to serve multiple tasks and the control of these tasks via scalar parameters, ensuring dynamic switching between tasks. The structure of the proposed model is grounded in a computational analysis to avoid inference latency. The working principle of the unified framework is explained with a theoretical subspace analysis based on singular value decomposition (SVD) and Grassmann distance. To support joint source-channel coding (JSCC) transmission for the proposed model, we develop a novel adaptability algorithm by adding binary masks. The numerical results demonstrate that the proposed unified framework achieves nearly the same performance as a dedicated model per task, while offering significant computational complexity savings and improved memory and storage efficiency. Additionally, the immense superiority of our framework is showcased over the simple approach of devising a backbone with multiple heads.