Spatio-temporal prediction supports radar/satellite nowcasting and city-scale traffic monitoring, but modern models are often too expensive for real-time deployment. This stems from a mismatch between dense computation and strong input-dependent redundancy (e.g., calm seas or clear skies). To enable automated, resource-aware architecture optimization in scalable media analysis, we propose Dyna-Pruner, an end-to-end framework for input-dependent co-pruning of data and model structure. A shared-importance synchronization mechanism generates coupled masks that prune redundant regions and their corresponding computational units (e.g., convolutional filters), yielding per-sample sparse sub-networks at inference time. Experiments on WeatherBench, SEVIR, and TaxiBJ show seamless integration with CNN, RNN, and Transformer backbones, reducing FLOPs by up to $70\%$ and achieving a $2.5\times$ speedup on NVIDIA Jetson AGX Orin with negligible accuracy loss ($<1\%$).

翻译：时空预测支持雷达/卫星临近预报及城市级交通监控，但现代模型因实时部署成本过高而受限。其根源在于密集计算与强输入依赖冗余（如平静海面或晴空场景）之间的失配。为实现可扩展媒体分析中的自动化资源感知架构优化，我们提出Dyna-Pruner——一种面向数据与模型结构输入依赖协同剪枝的端到端框架。其共享重要性同步机制可生成耦合掩码，对冗余区域及其对应计算单元（如卷积滤波器）进行剪枝，在推理阶段为每个样本生成稀疏子网络。在WeatherBench、SEVIR和TaxiBJ数据集上的实验表明，该框架可与CNN、RNN及Transformer主干网络无缝集成，将浮点运算量减少高达70%，并在NVIDIA Jetson AGX Orin上实现2.5倍加速，同时精度损失可忽略不计（<1%）。