Next-generation edge intelligence is anticipated to benefit various applications via offloading techniques. However, traditional offloading architectures face several issues, including heterogeneous constraints, partial perception, uncertain generalization, and lack of tractability. In this paper, we propose a Large AI Model-Based Offloading (LAMBO) framework with over one billion parameters for solving these problems. We first use input embedding (IE) to achieve normalized feature representation with heterogeneous constraints and task prompts. Then, we introduce a novel asymmetric encoder-decoder (AED) as the decision-making model, which is an improved transformer architecture consisting of a deep encoder and a shallow decoder for global perception and decision. Next, actor-critic learning (ACL) is used to pre-train the AED for different optimization tasks under corresponding prompts, enhancing the AED's generalization in multi-task scenarios. Finally, we propose an active learning from expert feedback (ALEF) method to fine-tune the decoder of the AED for tracking changes in dynamic environments. Our simulation results validate the advantages of the proposed LAMBO framework.

翻译：下一代边缘智能有望通过卸载技术惠及各类应用。然而，传统卸载架构面临诸多问题，包括异构约束、局部感知、泛化不确定性以及可追踪性不足。本文提出一种参数规模超十亿的大型AI模型驱动的卸载（LAMBO）框架以解决上述问题。我们首先采用输入嵌入（IE）技术，在异构约束与任务提示条件下实现归一化特征表示。随后，引入一种新颖的非对称编码器-解码器（AED）作为决策模型，该模型基于改进的Transformer架构，由深度编码器与浅层解码器构成，以实现全局感知与决策。接着，通过演员-评论家学习（ACL）对AED进行针对不同提示下优化任务的预训练，从而增强其在多任务场景中的泛化能力。最后，我们提出基于专家反馈的主动学习（ALEF）方法，对AED的解码器进行微调以追踪动态环境变化。仿真实验结果验证了所提LAMBO框架的优越性。