NeSSA: Near-Storage Data Selection for Accelerated Machine Learning Training
Neha Prakriya, Yu Yang, Baharan Mirzasoleiman, Cho‐Jui Hsieh, Jason Cong · 2023
Large-scale machine learning (ML) models rely on extremely large datasets to learn their exponentially growing number of parameters. While these models achieve unprecedented success, the increase in training time and hardware resources required is unsustainable. Further, we find that as dataset sizes increase, data movement becomes a significant component of overall training time. We propose NeSSA, a novel SmartSSD+GPU training architecture to intelligently select important subsets of large datasets near-storage, such that training on the subset mimics training on the full dataset with a very small loss in accuracy. To the best of our knowledge, this is the first work to propose such a near-storage data selection model for efficient ML training. We have evaluated our method for the CIFAR-10, SVHN, CINIC-10, CIFAR-100, TinyImageNet, and ImageNet-100 datasets. We also test across ResNet-20, ResNet-18, and ResNet-50 models.