To support on-device inference, the next-generation mobile networks are expected to support real-time model downloading services to mobile users. However, powerful AI models typically have large model sizes, resulting in excessive end-to-end (E2E) downloading-and-inference (DAI) latency. To address this issue, we propose a simultaneous model downloading and inference (SLIDE) framework, which allows users to perform inference with downloaded layers while simultaneously receiving the remaining layers of the model. To this end, we formulate a task throughput maximization problem by jointly optimizing model provisioning, spectrum bandwidth allocation, and computing resource allocation for multi-user downlink systems. Unlike traditional DAI frameworks, SLIDE introduces recursive dependencies across layers, where inference latency depends recursively on the downloading bandwidth and computing resource allocation for each of the preceding layers. To solve this challenging problem, we design an efficient algorithm that acquires the optimal solution with polynomial-time complexity. Simulation results demonstrate that the proposed SLIDE framework significantly improves task throughput under latency and communication resource constraints compared with the conventional model downloading schemes.

翻译：为支持设备端推理，下一代移动网络需为移动用户提供实时模型下载服务。然而，强大的AI模型通常具有较大规模，导致端到端下载与推理时延过高。为解决此问题，我们提出一种同步模型下载与推理框架，允许用户在下载部分模型层的同时，利用已下载层执行推理，并同步接收模型的剩余层。为此，我们针对多用户下行链路系统，通过联合优化模型配置、频谱带宽分配与计算资源分配，构建了任务吞吐量最大化问题。与传统下载与推理框架不同，SLIDE引入了层间的递归依赖关系，其中推理时延递归地取决于每一前序层的下载带宽与计算资源分配。为解决这一具有挑战性的问题，我们设计了一种高效算法，该算法能以多项式时间复杂度获得最优解。仿真结果表明，在时延与通信资源约束下，相较于传统模型下载方案，所提出的SLIDE框架能显著提升任务吞吐量。