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Training of deep learning pipelines on memory-constrained GPUs via segmented fused-tiled execution
2022
Proceedings of the 31st ACM SIGPLAN International Conference on Compiler Construction
Training models with massive inputs is a significant challenge in the development of Deep Learning pipelines to process very large digital image datasets as required by Whole Slide Imaging (WSI) in computational pathology and analysis of brain fMRI images in computational neuroscience. Graphics Processing Units (GPUs) represent the primary workhorse in training and inference of Deep Learning models. In order to use GPUs to run inference or training on a neural network pipeline, state-of-the-art
doi:10.1145/3497776.3517766
pmid:35876769
pmcid:PMC9302555
fatcat:4ghjtvtwtvg7tevoedi2wmgn4e