Toward Energy-Efficient AI: Development Methods and Python-Based Case Studies
DOI:
https://doi.org/10.15680/IJCTECE.2024.0706001Keywords:
Energy-Efficient AI, Python for AI, Sustainable AI Development, AI Model Optimization, Energy-Aware Training, Case Studies in AI, Carbon Footprint of AIAbstract
With the rapid development and adoption of artificial intelligence (AI), the environmental impact of energy consumption during the training and deployment of AI models has become a pressing concern. Energy-efficient AI development is crucial for reducing the carbon footprint of AI technologies. This paper explores various methods and tools used to optimize energy efficiency in AI model development, focusing on Python-based techniques. Case studies are presented to demonstrate practical applications of energy-efficient AI. By analyzing optimization strategies such as model pruning, quantization, hardware acceleration, and energy-aware training, this work highlights how Python libraries and frameworks can contribute to sustainable AI practices.
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