  Using medical imaging as case-study, we demonstrate how Intel-optimized
TensorFlow on an x86-based server equipped with 2nd Generation Intel Xeon
Scalable Processors with large system memory allows for the training of
memory-intensive AI/deep-learning models in a scale-up server configuration. We
believe our work represents the first training of a deep neural network having
large memory footprint (~ 1 TB) on a single-node server. We recommend this
configuration to scientists and researchers who wish to develop large,
state-of-the-art AI models but are currently limited by memory.
