  Soft error, namely silent corruption of signal or datum in a computer system,
cannot be caverlierly ignored as compute and communication density grow
exponentially. Soft error detection has been studied in the context of
enterprise computing, high-performance computing and more recently in
convolutional neural networks related to autonomous driving. Deep learning
recommendation systems (DLRMs) have by now become ubiquitous and serve billions
of users per day. Nevertheless, DLRM-specific soft error detection methods are
hitherto missing. To fill the gap, this paper presents the first set of
soft-error detection methods for low-precision quantized-arithmetic operators
in DLRM including general matrix multiplication (GEMM) and EmbeddingBag. A
practical method must detect error and do so with low overhead lest reduced
inference speed degrades user experience. Exploiting the characteristics of
both quantized arithmetic and the operators, we achieved more than 95%
detection accuracy for GEMM with an overhead below 20%. For EmbeddingBag, we
achieved 99% effectiveness in significant-bit-flips detection with less than
10% of false positives, while keeping overhead below 26%.
