  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.
