research
Green-NAS (Edge-native weather forecasting)
Multi-objective NAS for efficiency under real deployment constraints
Neural Architecture SearchGreen AIEdge ComputingWeather Forecasting
View project
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.
Links
- 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