Adaptive Inference: Theoretical Limits and Unexplored Opportunities
Soheil Hor Ying Qian Mert Pilanci Amin Arbabian
This paper introduces the first theoretical framework for quantifying the
efficiency and performance gain opportunity size of adaptive inference
algorithms. We provide new approximate and exact bounds for the achievable
efficiency and performance gains, supported by empirical evidence demonstrating
the potential for 10-100x efficiency improvements in both Computer Vision and
Natural Language Processing tasks without incurring any performance penalties.
Additionally, we offer insights on improving achievable efficiency gains
through the optimal selection and design of adaptive inference state spaces.