SmartMem: Memory Failure Prediction Challenge at WWW 2025

Uncorrectable Errors (UEs) of Dynamic Random Access Memory (DRAM) have been identified as a major failure cause in data centers and the failure of High Bandwidth Memory (HBM), which severely threatens the availability and reliability of cloud service, entire computing clusters, and web-based applications. Forecasting UEs before enacting preemptive maintenance measures has emerged as a viable strategy for diminishing server outages and some machine-learning-based solutions have also been proposed. However, predicting UEs presents several challenges: data noise and extreme imbalance as the UEs are exceedingly rare in memory events; heterogeneous data sources as the DRAMs in the field come from different manufacturing or architecture platforms; distribution shifts due to the hardware aging; and latent factors due to the dynamic access mechanism. These challenges become even more pronounced in web-centric contexts, where UE patterns can vary significantly across applications, including content delivery networks, e-commerce platforms, and real-time analytics.We developed a comprehensive real-world memory error dataset that includes both micro-level and bit-level information. This dataset presents a two-stage challenge aimed at devising a more efficient and generalized solution for event prediction. We believe the competition and dataset provide a breeding ground to foster discussions and further progress on several important research topics towards real-world ML applications. More details can be found in the competition homepage: https://hwcloud-ras.github.io/SmartMem.github.io/