The rapid growth of end-user AI applications, such as computer vision and generative AI, has led to immense data and processing demands often exceeding user devices' capabilities. Edge AI addresses this by offloading computation to the network edge, crucial for future services in 6G networks. However, it faces challenges such as limited resources during simultaneous offloads and the unrealistic assumption of homogeneous system architecture. To address these, we propose a research roadmap focused on profiling AI models, capturing data about model types, hyperparameters, and underlying hardware to predict resource utilisation and task completion time. Initial experiments with over 3,000 runs show promise in optimising resource allocation and enhancing Edge AI performance.

翻译：终端用户AI应用（如计算机视觉与生成式AI）的快速增长，导致数据与处理需求常超出用户设备能力。边缘AI通过将计算任务卸载至网络边缘来解决此问题，这对未来6G网络中的服务至关重要。然而，其面临诸多挑战，包括并发卸载时的资源受限，以及对同构系统架构的不切实际假设。为解决这些问题，我们提出一个聚焦于AI模型剖析的研究路线图，通过捕获模型类型、超参数及底层硬件数据，以预测资源利用率与任务完成时间。超过3,000次运行的初步实验表明，该方法在优化资源分配与提升边缘AI性能方面具有潜力。