PVF (Parameter Vulnerability Factor): A Quantitative Metric Measuring AI Vulnerability Against Parameter Corruptions

no code implementations • 2 May 2024 • Xun Jiao, Fred Lin, Harish D. Dixit, Joel Coburn, Abhinav Pandey, Han Wang, Venkat Ramesh, Jianyu Huang, Wang Xu, Daniel Moore, Sriram Sankar

PVF can provide pivotal insights to AI hardware designers in balancing the tradeoff between fault protection and performance/efficiency such as mapping vulnerable AI parameter components to well-protected hardware modules.

text-classification Text Classification

Evaluating and Enhancing Robustness of Deep Recommendation Systems Against Hardware Errors

no code implementations • 17 Jul 2023 • Dongning Ma, Xun Jiao, Fred Lin, Mengshi Zhang, Alban Desmaison, Thomas Sellinger, Daniel Moore, Sriram Sankar

Deep recommendation systems (DRS) heavily depend on specialized HPC hardware and accelerators to optimize energy, efficiency, and recommendation quality.

Recommendation Systems

PyGFI: Analyzing and Enhancing Robustness of Graph Neural Networks Against Hardware Errors

no code implementations • 7 Dec 2022 • Ruixuan Wang, Fred Lin, Daniel Moore, Sriram Sankar, Xun Jiao

Inspired by the inherent algorithmic resilience of DL methods, this paper conducts, for the first time, a large-scale and empirical study of GNN resilience, aiming to understand the relationship between hardware faults and GNN accuracy.

Recommendation Systems