Automated Numerical Stability Analysis of Deep Learning Operators
Finite-precision arithmetic unavoidably introduces numerical approximation errors. Numerical computations may use insufficient precision or an improper formulation, which leads to numerical instability. In this paper, we introduce a unified software tool for stochastic numerical validation of deep-learning operators. The tool follows CESTAC on supported exposed operations and uses an operator-level data-perturbation approximation for GEMM-like kernels. Our developed software not only enables numerical validation with a single computation pass but also detects the sources of numerical instability and provides numerical stability monitoring during deep learning training and inference. We ve...
| Authors | Xinye Chen |
|---|---|
| Primary category | math.NA |
| Published | 28 July 2026 |
| Last updated at source | 7 August 2026 |
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