DYNA-PRUNER: Input-Adaptive Data-Model Co-Pruning for Efficient and Scalable Spatio-Temporal Media Prediction
arXiv:2606.15346v1 Announce Type: cross Abstract: 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 coupl
The increasing complexity of spatio-temporal prediction models, coupled with growing demands for real-time deployment in resource-constrained environments, necessitates more efficient architectural solutions.
This research introduces an input-adaptive co-pruning framework that significantly reduces the computational expense of AI models for critical applications like weather forecasting and traffic monitoring, enabling broader, more efficient deployment.
AI models for spatio-temporal media prediction can become more dynamic and resource-efficient, adapting computation based on input data complexity rather than always running at peak capacity.
- · AI infrastructure providers
- · Real-time monitoring systems
- · Edge computing developers
- · Weather and climate prediction agencies
- · Developers of monolithic, fixed-architecture AI models
- · Hardware manufacturers reliant solely on brute-force compute scaling
More widespread and cost-effective deployment of sophisticated spatio-temporal AI models becomes feasible.
Reduced energy consumption for large-scale AI operations, aiding sustainability goals and lowering operational costs.
Enhanced resilience and accuracy of critical infrastructure and environmental monitoring systems due to deployable, adaptive AI.
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Read at arXiv cs.LG