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arXiv · 2402.04359

Adaptive Inference: Theoretical Limits and Unexplored Opportunities

Soheil Hor·Ying Qian·Mert Pilanci·Amin Arbabian
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Abstract

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.

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Soheil Hor, Ying Qian, Mert Pilanci, Amin Arbabian. 2024-02-06. Adaptive Inference: Theoretical Limits and Unexplored Opportunities. https://arxiv.org/abs/2402.04359

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