Session

DescriptionAs AI systems scale across heterogeneous platforms and massive GPU clusters, runtime orchestration and reliability become critical challenges. This session presents advances in fault-aware DNN evaluation, distributed scientific machine learning, intelligent runtime scheduling, and workload forecasting for large-scale AI infrastructure. The papers collectively showcase system-level strategies that improve robustness, efficiency, and scalability for next-generation AI deployments.