Arithmetic-Intensity-Guided Fault Tolerance for Neural Network Inference on GPUs
This work addresses fault tolerance for neural network inference in safety-critical domains like spacecraft, offering an incremental improvement over existing algorithm-based fault tolerance methods.
The paper tackles the problem of fault tolerance in neural network inference on GPUs by proposing an adaptive, arithmetic-intensity-guided approach that selects the most efficient algorithm-based fault tolerance scheme per layer, reducing execution-time overhead by 1.09–5.3× compared to traditional methods.
Neural networks (NNs) are increasingly employed in safety-critical domains and in environments prone to unreliability (e.g., soft errors), such as on spacecraft. Therefore, it is critical to impart fault tolerance to NN inference. Algorithm-based fault tolerance (ABFT) is emerging as an efficient approach for fault tolerance in NNs. We propose an adaptive approach to ABFT for NN inference that exploits untapped opportunities in emerging deployment scenarios. GPUs have high compute-to-memory-bandwidth ratios, while NN layers have a wide range of arithmetic intensities. This leaves some layers compute bound and others memory-bandwidth bound, but current approaches to ABFT do not consider these differences. We first investigate ABFT schemes best suited for each of these scenarios. We then propose intensity-guided ABFT, an adaptive, arithmetic-intensity-guided approach that selects the most efficient ABFT scheme for each NN layer. Intensity-guided ABFT reduces execution-time overhead by 1.09--5.3$\times$ across many NNs compared to traditional approaches to ABFT.