RIFT: A Scalable Methodology for LLM Accelerator Fault Assessment using Reinforcement Learning

Abstract--The massive scale of modern AI accelerators presents critical challenges to traditional fault assessment methodologies, which face prohibitive computational costs and provide poor coverage of critical failure modes. This paper introduces RIFT (Reinforcement Learning-guided Intelligent Fault T argeting), a scalable framework that automates the discovery of minimal, high-impact fault scenarios for efficient design-time fault assessment. RIFT transforms the complex search for worst-case faults into a sequential decision-making problem, combining hybrid sensitivity analysis for search space pruning with reinforcement learning to intelligently generate minimal, high-impact test suites. Evaluated on billion-parameter Large Language Model (LLM) workloads using NVIDIA A100 GPUs, RIFT achieves a 2.2 fault assessment speedup over evolutionary methods and reduces the required test vector volume by over 99% compared to random fault injection, all while achieving superior fault coverage. The proposed framework also provides actionable data to enable intelligent hardware protection strategies, demonstrating that RIFT -guided selective error correction code provides a 12.8 improvement in cost-effectiveness (coverage per unit area) compared to uniform triple modular redundancy protection. RIFT automatically generates UVM-compliant verification artifacts, ensuring its findings are directly actionable and integrable into commercial RTL verification workflows. The recent advent of Large Language Models (LLMs) with hundreds of billions of parameters has had a transformative impact on computing, but has also introduced unprecedented computational demands [1].