DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting

2024-03-05

arXiv.org

AI Summary

To address sensor resource constraints and uneven spatiotemporal data coverage caused by terrain and socioeconomic factors in Earth system forecasting, this paper proposes DynST—a dynamic spatiotemporal sparse training paradigm designed for industrial deployment. DynST enables online, iterative, and temporally consistent sensor deployment pruning at the data level, integrating dynamic merging, dimensional mapping, and iterative sparse training to effectively mitigate timestamp conflicts inherent in spatiotemporal pruning. Compared to static sensor deployment and full-data training, DynST substantially reduces data dependency and computational overhead while preserving high prediction accuracy, enhancing model generalization, and improving cross-regional transferability. To the best of our knowledge, this is the first work to systematically incorporate dynamic sparsity into the co-design of spatiotemporal sensor optimization and model training.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

The ever-increasing sensor service, though opening a precious path and providing a deluge of earth system data for deep-learning-oriented earth science, sadly introduce a daunting obstacle to their industrial level deployment. Concretely, earth science systems rely heavily on the extensive deployment of sensors, however, the data collection from sensors is constrained by complex geographical and social factors, making it challenging to achieve comprehensive coverage and uniform deployment. To alleviate the obstacle, traditional approaches to sensor deployment utilize specific algorithms to design and deploy sensors. These methods extit{dynamically adjust the activation times of sensors to optimize the detection process across each sub-region}. Regrettably, formulating an activation strategy generally based on historical observations and geographic characteristics, which make the methods and resultant models were neither simple nor practical. Worse still, the complex technical design may ultimately lead to a model with weak generalizability. In this paper, we introduce for the first time the concept of spatio-temporal data dynamic sparse training and are committed to adaptively, dynamically filtering important sensor distributions. To our knowledge, this is the extbf{first} proposal ( extit{termed DynST}) of an extbf{industry-level} deployment optimization concept at the data level. However, due to the existence of the temporal dimension, pruning of spatio-temporal data may lead to conflicts at different timestamps. To achieve this goal, we employ dynamic merge technology, along with ingenious dimensional mapping to mitigate potential impacts caused by the temporal aspect. During the training process, DynST utilize iterative pruning and sparse training, repeatedly identifying and dynamically removing sensor perception areas that contribute the least to future predictions.

Problem

• Optimal Sensor Placement
• Spatial-Temporal Data
• Geoscientific Data Collection

Innovation

• Dynamic Sparse Training
• Spatial Temporal Data
• Sensor Network Optimization