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，高性能AI模型通常规模庞大，导致端到端下载-推理（DAI）时延过高。针对该问题，本文提出模型下载与推理同步进行（SLIDE）框架，使用户可在下载模型其余层的同时，利用已下载层执行推理。为此，我们通过联合优化多用户下行系统的模型供给、频谱带宽分配及计算资源分配，构建任务吞吐量最大化问题。与传统DAI框架不同，SLIDE引入跨层的递归依赖关系——推理时延递归地取决于前序各层的下载带宽与计算资源分配。针对这一挑战性问题，我们设计了高效算法，可在多项式时间内获得最优解。仿真结果表明，与传统模型下载方案相比，所提SLIDE框架在时延和通信资源约束下显著提升了任务吞吐量。