Paper Contents
Abstract
Increased demand for machine learning applications brings focus on energy consumption and carbon emission produced by training and deploying such large-scale models. This paper discusses strategies and technologies for enhancing energy efficiency in machine learning to reduce AI systemscarbon footprint. Key topics within the study include the development of lightweight ML models, optimization techniques, and hardware innovations to minimize energy usage without sacrificing performance. Case studies and practical implementations demonstrate how energy-efficient ML can contribute to sustainable AI and meet global directions toward environmental challenges. This research also focuses on future directions aimed at reducing the ecological footprint of AI, therefore fostering a more sustainable technological landscape with advances in data-driven solutions.
Copyright
Copyright © 2024 RISHITA MAHESHWARI. This is an open access article distributed under the Creative Commons Attribution License.
