根据推理信心动态决定何时提前退出，实现低延迟高效率的语义通信。
Goal-oriented Communications based on Recursive Early Exit Neural Networks
- 基于层间递归预测动态判断是否提前退出计算
- 在边缘推理场景中实现性能、延迟与资源消耗的平衡
- 适合对实时性要求高的智能终端和无线环境
本文提出一种面向目标的语义通信新框架，利用递归早退出神经网络。核心包含两部分：首先，设计一种创新的早退出策略，通过分析各层预测置信度变化，动态划分计算任务，对置信度提升缓慢的样本及时卸载至服务器；其次，构建基于强化学习的在线优化框架，联合决策早退出位置、计算拆分及卸载策略，综合考虑无线条件、推理精度与资源开销。数值实验表明，该方法在边缘推理场景中具备良好适应性，能有效平衡性能、延迟与资源效率。
原文摘要 · Abstract (English)
This paper presents a novel framework for goal-oriented semantic communications leveraging recursive early exit models. The proposed approach is built on two key components. First, we introduce an innovative early exit strategy that dynamically partitions computations, enabling samples to be offloaded to a server based on layer-wise recursive prediction dynamics that detect samples for which the confidence is not increasing fast enough over layers. Second, we develop a Reinforcement Learning-based online optimization framework that jointly determines early exit points, computation splitting, and offloading strategies, while accounting for wireless conditions, inference accuracy, and resource costs. Numerical evaluations in an edge inference scenario demonstrate the method's adaptability and effectiveness in striking an excellent trade-off between performance, latency, and resource efficiency.
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