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Deep recommendation systems (DRS) heavily depend on specialized HPC hardwareand accelerators to optimize energy, efficiency, and recommendation quality.Despite the growing number of hardware errors observed in large-scale fleetsystems where DRS are deployed, the robustness of DRS has been largelyoverlooked. This paper presents the first systematic study of DRS robustnessagainst hardware errors. We develop Terrorch, a user-friendly, efficient andflexible error injection framework on top of the widely-used PyTorch. Weevaluate a wide range of models and datasets and observe that the DRSrobustness against hardware errors is influenced by various factors from modelparameters to input characteristics. We also explore 3 error mitigation methodsincluding algorithm based fault tolerance (ABFT), activation clipping andselective bit protection (SBP). We find that applying activation clipping canrecover up to 30% of the degraded AUC-ROC score, making it a promisingmitigation method.

Evaluating and Enhancing Robustness of Deep Recommendation Systems Against Hardware Errors