# Efficient Soft-Error Detection for Low-precision Deep Learning Recommendation Models

arXiv: https://arxiv.org/abs/2103.00130 · HTML: https://mlsanity.com/paper/2103.00130/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 27 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems](https://mlsanity.com/paper/2003.05622/) (2020)
- [FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference](https://mlsanity.com/paper/2101.05615/) (2021)
- [Silent Data Corruptions at Scale](https://mlsanity.com/paper/2102.11245/) (2021)
- [Post-Training 4-bit Quantization on Embedding Tables](https://mlsanity.com/paper/1911.02079/) (2019)
- [ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Neural Network Accelerators](https://mlsanity.com/paper/1802.03806/) (2018)
- [Making Convolutions Resilient via Algorithm-Based Error Detection Techniques](https://mlsanity.com/paper/2006.04984/) (2020)
- [Improving Performance of Iterative Methods by Lossy Checkponting](https://mlsanity.com/paper/1804.11268/) (2018)

## Built on by (most cited first, 4 of 4)

- [ATTNChecker: Highly-Optimized Fault Tolerant Attention for Large Language Model Training](https://mlsanity.com/paper/2410.11720/) (2024)
- [Arithmetic-Intensity-Guided Fault Tolerance for Neural Network Inference on GPUs](https://mlsanity.com/paper/2104.09455/) (2021)
- [Evaluating and Enhancing Robustness of Deep Recommendation Systems Against Hardware Errors](https://mlsanity.com/paper/2307.10244/) (2023)
- [Exploring Winograd Convolution for Cost-effective Neural Network Fault Tolerance](https://mlsanity.com/paper/2308.08230/) (2023)

## Abstract (arXiv)

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%.