Scaling Neural Network Verification with Tensor Parallelism and Fully Sharded Data Parallelism
arXiv:2606.09377v1 Announce Type: new Abstract: Formal neural network verification -- proving that a network satisfies safety properties for \emph{all} inputs in a specified domain -- is bounded in practice by GPU memory: standard implementations of bound-propagation algorithms (IBP, CROWN, $\alpha$-CROWN) require weight and relaxation-coefficient matrices to reside entirely on one accelerator. We adapt two parallelism techniques originally developed for large-scale model training to the \texttt{auto\_LiRPA}\,/\,$\alpha,\beta$-CROWN verification framework. \textbf{Tensor Parallelism (TP)} shar
The increasing scale and complexity of neural networks necessitate more efficient verification methods to ensure reliability, especially as AI applications become mission-critical.
This research addresses a fundamental limitation in formal neural network verification, enabling the security and reliability of larger, more complex AI models.
The ability to formally verify very large neural networks is significantly enhanced, moving beyond current GPU memory constraints and broadening the scope of AI safety guarantees.
- · AI safety researchers
- · Developers of large AI models
- · Sectors requiring high-assurance AI (e.g., autonomous vehicles, defense)
Verification of state-of-the-art neural networks becomes more feasible and widespread.
Increased trust and adoption of AI in safety-critical applications due to stronger verification guarantees.
Accelerated development of even larger and more complex AI models with integrated safety from design rather than post-hoc remediation.
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Read at arXiv cs.LG