Journal Article

Inspiring from Galaxies to Green AI in Earth: Benchmarking Energy-Efficient Models for Galaxy Morphology Classification

Gkouvrikos, Emmanouil V., Georgousis, Ilias, Alevizos, Vasileios, Papakostas, George A., Karipidou, Sotiria

2025

Overview

Recent advancements in space exploration have significantly increased the volume of astronomical data, heightening the demand for efficient analytical methods. Concurrently, the considerable energy consumption of machine learning (ML) has fostered the emergence of Green AI, emphasizing sustainable, energy-efficient computational practices. We introduce the first large-scale Green AI benchmark for galaxy morphology classification, evaluating over 30 machine learning architectures (classical, ensemble, deep, and hybrid) on CPU and GPU platforms using a balanced subset of the Galaxy Zoo dataset. Beyond traditional metrics (precision, recall, and F1-score), we quantify inference latency, energy consumption, and carbon-equivalent emissions to derive an integrated EcoScore that captures the trade-off between predictive performance and environmental impact. Our results reveal that a GPU-optimized multilayer perceptron achieves state-of-the-art accuracy of 98% while emitting 20× less CO2 than ensemble forests, which—despite comparable accuracy—incur substantially higher energy costs. We demonstrate that hardware–algorithm co-design, model sparsification, and careful hyperparameter tuning can reduce carbon footprints by over 90% with negligible loss in classification quality. These findings provide actionable guidelines for deploying energy-efficient, high-fidelity models in both ground-based data centers and onboard space observatories, paving the way for truly sustainable, large-scale astronomical data analysis.

• Publisher: MDPI AG
• Subject: Accuracy / Algorithms / Artificial intelligence / astronomical image analysis / benchmarking performance / Benchmarks / Carbon / Carbon equivalent / carbon footprint in AI / Celestial bodies / Classification / Clean energy / Co-design / computer vision / Data analysis / Datasets / Discovery and exploration / Ecological footprint / Energy consumption / Energy costs / Energy management systems / energy-efficient machine learning / Environmental impact / Galaxies / green AI / Machine learning / Morphology / Multilayer perceptrons / Observatories / Outer space / Space exploration / Stars & galaxies / Supply and demand / Sustainability / Zoos