Brief Industry Paper: Evaluating Robustness of Deep Learning-Based Recommendation Systems Against Hardware Errors: A Case Study-Reference-Cited by-同舟云学术

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Brief Industry Paper: Evaluating Robustness of Deep Learning-Based Recommendation Systems Against Hardware Errors: A Case Study

Published:2023-12-05 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Real-Time Systems Symposium (RTSS)
language:
Short-container-title:

Author:

Jiao Xun¹,Lin Fred²,Xiao Matt²,Desmaison Alban²,Moore Daniel²,Sankar Sriram²

Affiliation:

1. Villanova University

2. Meta Platforms, Inc.

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10405422/10405425/10406075.pdf?arnumber=10406075

Reference21 articles.

1. Detection and prevention of silent data corruption in an exabyte-scale database system;Bacon;SELSE,2022

2. Detecting silent data corruptions in the wild;Dixit;arXiv preprint,2022

3. Razor: circuit-level correction of timing errors for low-power operation

4. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

5. FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation

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