Adaptive Inference: Theoretical Limits and Unexplored Opportunities

TL;DR

This work formalizes adaptive inference by modeling inference pipelines as adaptation state machines and introducing Adaptive Oracles to bound efficiency and accuracy. It provides both exact and constant-$\alpha$ approximate bounds, and validates them with ImageNet and HellaSwag experiments showing substantial potential for efficiency gains (up to tens to hundreds of times) without sacrificing performance. The results offer practical design guidelines for selecting state spaces (range, size, granularity) to maximize adaptation potential, and reveal near-optimal gains even with small state spaces. The framework lays a foundation for systematic, quantifiable design of adaptive inference in CV and NLP, with implications for edge deployment and cloud-scale efficiency.

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.

Figures (6)

• Figure 1: Confusion matrix for a conceptual 2-state classification task. Resource consumption of the Adaptive Oracle is only a function of the $P(IA)$
• Figure 2: Empirical Measurements of $\alpha_i$ for different tasks and models. $\alpha_i$ remains relatively constant for models with similar architecture.
• Figure 3: Benchmarks and corresponding $\alpha=1$ bounds. The shaded area shows the space of operation points achievable using adaptive inference techniques. The state of the art (SOTA) baseline is used as a proxy for the inherent efficiency versus performance trade-off of each task.
• Figure 4: Proposed $constant-\alpha$ bounds versus adaptation gain opportunity space empirically measured for an adaptive Oracle. The shaded area shows the space of operation points potentially achievable by an adaptive inference algorithm.
• Figure 5: Optimum discrete state spaces of different size and corresponding $R_{ratio}$ for the ImageNet SOTA. The red dot shows the state with the most utility relative to the immediately smaller state space.

Theorems & Definitions (1)

• Definition 2.1