Quantizing Time-Series Models As Dynamical Systems: Trajectory-Based Quantization Sensitivity Score

2026-06-11 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Mathematics & Computational Sciences · Depth: Expert, quick

Summary

We introduce the Trajectory-based Quantization Sensitivity Score (TQS), a metric that reframes post-training quantization (PTQ) through the lens of dynamical-systems stability. TQS models a network's rollout as a discrete-time dynamical system, characterizing how quantization-induced errors propagate and amplify over the rollout horizon. This approach enables a priori sensitivity estimation, decoupled from specific quantizer selection and bit-width assignment, allowing for quantization budget planning even for black-box or compiled networks with fused operators. Building on TQS, the authors present TQS-PTQ, a flexible mixed-precision framework that requires no calibration data or costly second-order approximations. Experiments demonstrate that this dynamical-systems perspective offers a robust, high-performing pathway for low-precision deployment in resource-constrained settings.

Key takeaway

For Machine Learning Engineers optimizing time-series models for resource-constrained environments, TQS-PTQ offers a novel approach to post-training quantization. You can now estimate quantization sensitivity a priori, even for black-box models, enabling more effective budget planning and mixed-precision deployment without needing calibration data. This method provides a robust pathway to achieve low-precision inference while maintaining high performance.

Key insights

Reframing post-training quantization as a dynamical system allows a priori error sensitivity estimation for robust low-precision deployment.

Principles

• Quantization errors propagate dynamically.
• Decouple sensitivity from quantizer choice.
• Dynamical systems offer robust PTQ.

Method

TQS models network rollout as a discrete-time dynamical system to characterize error propagation. TQS-PTQ then uses this score for flexible mixed-precision quantization without calibration data or second-order approximations.

In practice

• Plan quantization budgets for black-box models.
• Deploy low-precision models in resource-constrained settings.
• Apply mixed-precision without calibration data.

Topics

• Post-Training Quantization
• Time-Series Models
• Dynamical Systems
• Mixed-Precision Quantization
• Resource-Constrained Deployment
• Quantization Sensitivity Score

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.