Collaborative Edge Inference with Dynamic Task Offloading and Early Exiting

This December 2024 paper introduces a collaborative inference framework designed for large-scale models in 5G smart city edge computing environments, addressing the challenge of limited memory and computing capacity on individual edge nodes. The framework partitions large models into sub-models deployed across multiple edge nodes and incorporates an early exit mechanism to accelerate inference. To manage the complexities of heterogeneous systems and dynamic environments, the authors propose a distributed algorithm called DTO-EE, which jointly optimizes task offloading strategies and confidence thresholds for early exits. Experimental results demonstrate that DTO-EE significantly reduces response delay and improves inference accuracy compared to existing methods.Source:https://arxiv.org/pdf/2412.08284

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