Adaptive Inference: Theoretical Limits and Unexplored Opportunities
This foundational work addresses the efficiency bottleneck in adaptive inference for ML/AI, offering a theoretical framework that could broadly impact model deployment.
The paper tackles the problem of quantifying the efficiency and performance gains of adaptive inference algorithms, providing theoretical bounds and empirical evidence showing potential for 10-100x efficiency improvements in Computer Vision and Natural Language Processing tasks without performance loss.
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.