We introduce the Trajectory-based Quantization Sensitivity Score (TQS), a metric that reframes post-training quantization (PTQ) through the lens of dynamical-systems stability. By modeling the network's rollout as a discrete-time dynamical system, TQS characterizes how quantization-induced errors propagate and amplify over the rollout horizon. Unlike conventional PTQ methods, where sensitivity analysis is often coupled to the quantization procedure, TQS enables a priori sensitivity estimation decoupled from quantizer selection and bit-width assignment. This separation allows for quantization budget planning even for black-box or compiled networks with fused operators. Building on this, we present TQS-PTQ, a flexible mixed-precision framework that requires no calibration data or costly second-order approximations. Our experiments show that a dynamical-systems perspective provides a robust, high-performing pathway for low-precision deployment in resource-constrained settings.

翻译：我们提出基于轨迹的量化敏感度评分（TQS），这是一种通过动态系统稳定性视角重构训练后量化（PTQ）的度量指标。通过将网络的时序展开建模为离散时间动态系统，TQS可刻画量化误差在展开过程中如何传播与放大。与传统的PTQ方法不同（其敏感度分析通常与量化过程耦合），TQS实现了与量化器选择及位宽分配解耦的先验敏感度估计。这种分离性使得即使对于包含融合算子的黑盒网络或编译网络，也能进行量化预算规划。在此基础上，我们提出TQS-PTQ——一种无需校准数据或昂贵二阶近似的灵活混合精度框架。实验表明，动态系统视角为资源受限场景下的低精度部署提供了稳健且高性能的路径。