Computer Science > Information Retrieval
arXiv:2307.10244 (cs)
[Submitted on 17 Jul 2023]
Title:Evaluating and Enhancing Robustness of Deep Recommendation Systems Against Hardware Errors
Authors:Dongning Ma,Xun Jiao,Fred Lin,Mengshi Zhang,Alban Desmaison,Thomas Sellinger,Daniel Moore,Sriram Sankar
View a PDF of the paper titled Evaluating and Enhancing Robustness of Deep Recommendation Systems Against Hardware Errors, by Dongning Ma and 7 other authors
View PDFAbstract:Deep recommendation systems (DRS) heavily depend on specialized HPC hardware and accelerators to optimize energy, efficiency, and recommendation quality. Despite the growing number of hardware errors observed in large-scale fleet systems where DRS are deployed, the robustness of DRS has been largely overlooked. This paper presents the first systematic study of DRS robustness against hardware errors. We develop Terrorch, a user-friendly, efficient and flexible error injection framework on top of the widely-used PyTorch. We evaluate a wide range of models and datasets and observe that the DRS robustness against hardware errors is influenced by various factors from model parameters to input characteristics. We also explore 3 error mitigation methods including algorithm based fault tolerance (ABFT), activation clipping and selective bit protection (SBP). We find that applying activation clipping can recover up to 30% of the degraded AUC-ROC score, making it a promising mitigation method.
| Subjects: | Information Retrieval (cs.IR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2307.10244 [cs.IR] |
| (orarXiv:2307.10244v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2307.10244 arXiv-issued DOI via DataCite |
Full-text links:
Access Paper:
- View PDF
- TeX Source
- Other Formats
View a PDF of the paper titled Evaluating and Enhancing Robustness of Deep Recommendation Systems Against Hardware Errors, by Dongning Ma and 7 other authors
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.