Development and Assessment of Energy-Efficient Approaches for AI-Based Green Computing

In response to the critical demand for energy-efficient solutions within AI-based green computing, this study endeavors to tackle this imperative challenge. Within the ambit of natural language processing, an inventive dimensional reduction approach is introduced, which, in a pioneering fashion, facilitates the conversion of complex multiclass problems into singular target problems. This groundbreaking technique effectively curtails computation time, optimizes the judicious utilization of computational resources, all the while upholding the preeminent standard of accuracy. In the domain of image processing, an adroitly designed custom loss function, named focal loss, is harmoniously integrated with the dimensional reduction approach, resulting in a significant diminution of computational time without any compromise on the hallowed altar of accuracy. To gauge the energy efficiency of these visionary approaches, an ingenious metric christened “efficiency” is proffered, proffering an all-encompassing evaluation of energy consumption. The empirical findings unequivocally underscore the colossal potential inherent in these pioneering approaches, as they contribute to the augmentation of response times in AI models catering to the domains of natural language processing and computer vision. Concurrently, they effectuate a reduction in energy consumption, thereby advancing the objectives of green computing.