时序模型部署时量化误差会随时间累积，TQS用动力系统理论解决了这一痛点，做边缘设备或IoT部署的工程师可以直接参考。
该论文提出了一种名为轨迹量化敏感度分数（TQS）的新指标，将时序模型的量化问题重新定义为动力系统的稳定性分析。TQS通过将模型推理视为离散时间动力系统，量化了量化误差在时间步上的传播和放大效应。与传统的后训练量化方法不同，TQS可以独立于量化器选择和位宽分配进行敏感度估计，适用于黑盒或编译后的网络。基于TQS，作者提出了TQS-PTQ框架，无需校准数据或二阶近似即可实现混合精度量化。实验表明，该视角在资源受限场景下提供了稳健且高性能的低精度部署方案。
Quantizing Time-Series Models As Dynamical Systems: Trajectory-Based Quantization Sensitivity Score
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.