Quantizing Time-Series Models As Dynamical Systems: Trajectory-Based Quantization Sensitivity Score
arXiv:2606.13300v2 Announce Type: replace Abstract: 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 a
The increasing complexity and deployment of AI models, particularly for real-time and embedded applications, demand more efficient and robust quantization methods.
This development offers a novel, theoretical approach to optimizing model quantization, which is critical for reducing computational requirements and facilitating broader AI deployment.
The ability to estimate quantization sensitivity decoupled from specific quantizer selection and bit-width allows for more efficient and effective post-training quantization strategies.
- · AI hardware manufacturers
- · Edge AI developers
- · Researchers in model optimization
- · Industries deploying AI in resource-constrained environments
- · Developers reliant on ad-hoc quantization methods
- · Cloud AI providers facing increased edge competition
Improved efficiency and reduced errors in quantized AI models for production.
Expansion of AI applications into new domains due to lower computational and energy footprints.
Accelerated innovation in AI hardware and software co-design paradigms driven by deeper understanding of quantization effects.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG