Multiscale Modeling and Data-Driven Approaches for Energy Efficiency Prediction in Computing

The growing demand for sustainable computing has spurred research efforts to develop energy-efficient computing materials. This study explores multiscale modeling and data-driven approaches for energy efficiency prediction in computing materials, aiming to advance sustainable computing practices. Multiscale modeling allows for a comprehensive analysis of material properties and behavior across different lengths and time scales, enabling a deeper understanding of energy consumption mechanisms. Data-driven approaches use machine learning algorithms to model and predict energy efficiency based on empirical data. The study begins by addressing the challenges associated with performance prediction for sustainable computing materials, highlighting the limitations of traditional modeling techniques. It then analyzes the methodologies of multiscale modeling, which encompass atomistic simulations, mesoscale modeling, and continuum-level simulations. These modeling approaches facilitate a holistic understanding of the relationships between material structure, properties, and energy consumption. Next, the study explores the application of data-driven approaches, such as support vector machines (SVM), Gaussian processes, and neural networks, for energy efficiency prediction. The research work provides a step-by-step algorithmic explanation of SVM, emphasizing its suitability for energy efficiency prediction and its advantages in terms of accuracy and interpretability. In conclusion, it underscores the importance of integrating multiscale modeling and data-driven approaches to address the challenges of energy efficiency prediction in computing materials. By combining computational modeling, experimental data, and machine learning algorithms, the study can better understand energy consumption mechanisms and drive advancements in sustainable computing.