Computational Budget

Computational budget in AI focuses on optimizing the resource usage (energy, time, hardware) of machine learning models while maintaining performance. Current research emphasizes adaptive computation allocation, tailoring resource use to individual inputs or tasks, and exploring efficient model architectures and training techniques like using smaller models, optimized precision (e.g., bfloat16), and efficient data augmentation. This research is crucial for deploying AI models across diverse hardware and for mitigating the environmental impact of increasingly large and complex models, impacting both the sustainability and accessibility of AI applications.