Green Algorithms: Optimizing Computational Efficiency for Sustainable AI
Abstract
As artificial intelligence becomes increasingly resource-intensive, its environmental impact has grown considerably. This paper explores the concept of green algorithms, focusing on designing and deploying AI models that balance performance with energy efficiency. We analyze carbon footprints across training and inference stages, introduce optimization techniques like model pruning, quantization, and federated learning, and propose a sustainability metric for algorithm development. The study demonstrates that responsible innovation in AI can significantly reduce ecological costs without compromising utility, laying a foundation for eco-conscious computing practices.
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