Adaptive Inference: Theoretical Limits and Unexplored Opportunities

• URL: http://arxiv.org/abs/2402.04359v1
• Date: Tue, 6 Feb 2024 19:49:23 GMT
• Title: Adaptive Inference: Theoretical Limits and Unexplored Opportunities
• Authors: Soheil Hor, Ying Qian, Mert Pilanci, Amin Arbabian
• Abstract summary: We provide new approximate and exact bounds for the achievable efficiency and performance gains. We offer insights on improving achievable efficiency gains through the optimal selection and design of adaptive inference state spaces.
• Score: 43.19515571173486
• License: http://creativecommons.org/licenses/by/4.0/
• 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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