论文标题
解决AI模型培训中的记忆瓶颈
Addressing the Memory Bottleneck in AI Model Training
论文作者
论文摘要
使用医学成像作为病例研究，我们演示了配备了X86基于X86的服务器上的Intel优化的Tensorflow，该服务器配备了第二代Intel Xeon可伸缩处理器，具有较大的系统存储器，可以训练以扩展服务器配置的内存密集型AI/深度学习模型。我们相信我们的工作代表了一个深神经网络的首次培训，该网络具有大型内存足迹（〜1 tb）。我们向希望开发大型，最先进的AI模型但目前受记忆限制的科学家和研究人员建议这种配置。
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.
