Enhancing safety and resilience in AI-driven systems: from autonomous vehicles to data centers

Artificial intelligence (AI) has been widely adopted due to its advanced capabilities, finding applications in autonomous vehicles and, more recently, in large language models (LLMs) powered by transformers. As AI systems continue to expand in scope and influence, ensuring their reliability and robustness becomes increasingly critical, particularly in unforeseen scenarios where failures can have significant consequences. Furthermore, given the substantial computational and economic costs associated with training and deploying large-scale models, which often require extensive GPU clusters, the underlying infrastructure must be highly fault-tolerant to maintain efficiency and stability. This thesis presents our work on iPrism, a framework designed to enhance the safety of autonomous vehicles by reducing collision rates through AI-driven risk assessment and mitigation. This thesis also discusses the work Characterizing GPU Resilience and Impact on AI/HPC Systems, which examines GPU failure patterns in the Delta Supercomputer to improve system reliability.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Chen, Ziheng

Contributors dc:contributor

• Iyer, Ravishankar K.

• Dependable Systems
• Autonomous Vehicles
• Gpu Resilience
• High-performance Computing

Statement dc:rights

• Copyright 2025 Ziheng Chen

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/129342

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F129342