Energy-Efficient Machine Learning: Techniques and Applications for Sustainable AI
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Abstract
The growing computational demands of machine learning models have raised concerns about energy consumption and environmental impact. This paper explores techniques for developing energy-efficient machine learning systems, including model compression, hardware optimization, and green data centers. We present a comparative analysis of energy usage across various algorithms and architectures, highlighting trade-offs between performance and efficiency. Applications in energy-constrained environments, such as edge computing and IoT, are discussed. The findings emphasize the importance of sustainable AI practices to balance innovation with environmental responsibility.
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