Scaling Neural Network Verification with Tensor Parallelism and Fully Sharded Data Parallelism

Researchers have adapted GPU parallelism techniques to neural network verification, enabling formal safety proofs on larger models. Fully Sharded Data Parallelism (FSDP) reduces memory usage by 80-90% while maintaining identical verification results, though Tensor Parallelism trades some bound quality for memory efficiency.

Neural network verification—the process of formally proving that AI models behave safely across all possible inputs—faces a critical bottleneck: GPU memory constraints. Standard verification algorithms (IBP, CROWN, α-CROWN) require massive weight and relaxation matrices to fit entirely on single accelerators, limiting scalability. This research addresses that constraint by borrowing parallelism strategies from large-scale model training and adapting them to the auto_LiRPA/α,β-CROWN verification framework.

The work distinguishes between two approaches: Tensor Parallelism (TP) distributes both weight and activation matrices across GPUs, achieving roughly 2× peak-memory reduction but degrading bound tightness due to forced IBP substitution in sharded zones. Fully Sharded Data Parallelism (FSDP) takes a more conservative approach, sharding only weights with per-layer AllGather operations. Crucially, FSDP produces results bitwise identical to single-GPU baselines—preserving soundness and bound quality—while cutting baseline memory by 80-90% and peak memory by 34-39% on wide MLPs.

For the AI safety and verification community, FSDP integration with complete verification (β-CROWN + Branch-and-Bound) and convolutional layers represents meaningful progress. The successful unsat result on CIFAR-100 ResNet-large demonstrates practical capability on realistic benchmarks. The discovery that per-neuron alpha tensors, not weight matrices, become the memory bottleneck in α-CROWN+BaB mode reshapes future optimization priorities.

This work enables verification of larger, more complex neural networks without proportional hardware scaling, directly supporting the push toward formally certified AI safety in critical applications.

• →FSDP reduces peak GPU memory by 34-39% on wide MLPs while preserving bitwise-identical verification results to baseline methods
• →Tensor Parallelism achieves 2× peak-memory reduction but trades bound tightness for memory efficiency due to IBP substitution
• →Per-neuron alpha tensors, not weight matrices, emerge as the primary memory bottleneck in complete verification workflows
• →FSDP integrates successfully with convolutional layers and complete verification, enabling formal proofs on large networks like CIFAR-100 ResNet
• →Parallelism techniques from large-scale training can be adapted to formal verification without compromising soundness