Automated Numerical Stability Analysis of Deep Learning Operators
Quick summary
arXiv:2607.25494v3 Announce Type: replace-cross Abstract: 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 computatio
Key takeaways
- arXiv:2607.25494v3 Announce Type: replace-cross Abstract: 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.
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
Member comments