Energy Efficient and Sustainable Computing Models for Intelligent Autonomous Systems A Comparative Analysis

Energy-Efficient and Sustainable Computing Models for Intelligent Autonomous Systems Research Description This research paper presents a comparative analysis of energy-efficient and sustainable computing models for intelligent autonomous systems. The study investigates the relationship between: Autonomy Levels AI Model Architectures Training Time Energy Consumption CO₂ Emissions Research Methodology Historical benchmark datasets and simulation-based prediction techniques were used to estimate future sustainability challenges associated with AI systems. "Higher autonomy levels significantly increase computational complexity, energy consumption, and environmental impact." Key Findings High-autonomy systems require longer training time. Energy usage increases with deeper architectures. Lightweight AI models improve sustainability. Green AI practices reduce environmental impact. Technologies & Concepts Artificial Intelligence Machine Learning Green Computing Sustainable AI Autonomous Systems Conclusion The findings support the development of environmentally responsible and energy-efficient intelligent systems aligned with long-term sustainability goals. Read Full Research Paper Authors: Aayan Abdul Mannan Shaikh, Rishi Poddar, Ms. Jyotsna Anthal