Amortized-Precision Quantization for Early-Exit Vision Transformers

Researchers introduce Amortized-Precision Quantization (APQ) and MAQEE, a framework that optimizes Vision Transformers for low-precision deployment with early-exit mechanisms. By jointly optimizing exit thresholds and bit-widths while accounting for quantization noise across layers, the approach achieves up to 95% reduction in computational operations while maintaining accuracy across vision tasks.

This research addresses a critical challenge in deploying Vision Transformers at scale: combining quantization (reducing numerical precision) with early-exit inference (stopping computation early when confident enough) without sacrificing accuracy. The core problem stems from existing quantization methods treating models as static full-depth systems, which fails when early-exit decisions become unstable due to quantization noise, creating cascading errors through dynamic inference paths.

The technical contribution centers on recognizing that different layers experience varying exposure to quantization effects depending on exit probability distributions. APQ formulates this as a utilization-aware problem, revealing depth-precision trade-offs that prior methods overlooked. MAQEE builds on this insight by jointly optimizing both exit thresholds and bit-widths as interdependent parameters, using explicit risk control to maintain inference stability.

For the AI infrastructure sector, these results matter significantly. Achieving 95% reduction in Bit Operations (BOPs) while sustaining accuracy translates directly to lower latency, reduced memory consumption, and decreased energy usage—critical for deploying ViTs in resource-constrained environments like edge devices and mobile platforms. The consistent 20% improvement over baselines across diverse tasks (classification, detection, segmentation) demonstrates generalizability.

The framework's bi-level optimization approach establishes a stronger Pareto frontier, enabling practitioners to choose operating points along the accuracy-efficiency spectrum. This flexibility is particularly valuable for applications with heterogeneous hardware constraints. Future work likely extends these principles to other model architectures and explores hardware-aware quantization schemes that leverage these theoretical insights for practical deployments.

• →APQ reveals that quantization noise affects early-exit decisions unevenly across layers, requiring joint optimization of exit thresholds and bit-widths.
• →MAQEE achieves up to 95% reduction in computational operations while maintaining accuracy across vision classification, detection, and segmentation tasks.
• →The framework outperforms baseline methods by up to 20%, establishing a superior accuracy-efficiency trade-off frontier.
• →Joint optimization of exit thresholds and precision addresses a fragility gap in existing quantization methods applied to early-exiting models.
• →Results generalize across multiple vision tasks, suggesting broad applicability to Vision Transformer deployment in resource-constrained environments.