Scaling Neural Network Verification with Tensor Parallelism and Fully Sharded Data Parallelism
Formal neural network verification -- proving that a network satisfies safety properties for *all* inputs in a specified domain -- is bounded in practice by GPU memory: standard implementations of bound-propagation algorithms (IBP, CROWN, $α$-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 auto_LiRPA / $α,β$-CROWN verification framework. Tensor Parallelism (TP) shards both weight and $A$-matrices across GPUs, achieving ${\approx}2\times$ peak-memory reduction at $P{=}2$; soundness is confirmed on VNN-COMP 2022 MNIST-FC benchmarks, though bound tig...
| Authors | Sergei Vorobyov, Eugene Ilyushin |
|---|---|
| Primary category | cs.LG |
| Published | 8 June 2026 |
| Last updated at source | 9 June 2026 |
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