Arithmetic-Intensity-Guided Fault Tolerance for Neural Network Inference on GPUs

TL;DR

This paper introduces an adaptive fault tolerance method for neural network inference on GPUs, leveraging arithmetic intensity to optimize error correction schemes and significantly reduce overhead.

Contribution

It proposes an intensity-guided ABFT approach that dynamically selects the best fault tolerance scheme based on layer compute or memory bound nature.

Findings

Reduces execution-time overhead by up to 5.3×

Effectively adapts fault tolerance to layer characteristics

Improves reliability in safety-critical NN applications

Abstract

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.