Fault Aware

Fault-aware computing focuses on designing and training deep learning models that are resilient to hardware failures, a critical issue as models scale to massive sizes and deploy on increasingly unreliable hardware. Current research emphasizes techniques like adaptive resource allocation (e.g., for Mixture-of-Experts models), pipeline adaptation to handle failures in distributed training, and fault-aware quantization to mitigate the impact of permanent hardware faults in specialized accelerators. These advancements aim to improve the reliability and efficiency of large-scale deep learning training and deployment, impacting both the development of robust AI systems and the design of efficient hardware architectures.