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Green-NAS (Edge-native weather forecasting)

Multi-objective NAS for efficiency under real deployment constraints

Neural Architecture SearchGreen AIEdge ComputingWeather Forecasting
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Problem

Many forecasting models are designed assuming abundant compute, but practical deployments (especially in low-resource settings) must operate under constraints like limited memory, low power, and latency requirements.

Approach

Green-NAS frames architecture search as a multi-objective optimization problem:

  • maintain strong predictive performance
  • aggressively control model size and efficiency targets aligned with “Green AI” principles

Key result (high-level)

The best discovered model achieves competitive forecasting accuracy while using orders of magnitude fewer parameters than large, globally deployed systems—making deployment feasible on constrained hardware.

  • Paper (arXiv): https://arxiv.org/abs/2602.00240
  • PDF: https://arxiv.org/pdf/2602.00240.pdf

Reproducibility checklist (what I aim to provide)

  • dataset + preprocessing notes
  • baseline configuration
  • search space definition
  • evaluation metrics + reporting template