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The most widely used machine learning frameworks require users to carefullytune their memory usage so that the deep neural network (DNN) fits into theDRAM capacity of a GPU. This restriction hampers a researcher's flexibility tostudy different machine learning algorithms, forcing them to either use a lessdesirable network architecture or parallelize the processing across multipleGPUs. We propose a runtime memory manager that virtualizes the memory usage ofDNNs such that both GPU and CPU memory can simultaneously be utilized fortraining larger DNNs. Our virtualized DNN (vDNN) reduces the average GPU memoryusage of AlexNet by up to 89%, OverFeat by 91%, and GoogLeNet by 95%, asignificant reduction in memory requirements of DNNs. Similar experiments onVGG-16, one of the deepest and memory hungry DNNs to date, demonstrate thememory-efficiency of our proposal. vDNN enables VGG-16 with batch size 256(requiring 28 GB of memory) to be trained on a single NVIDIA Titan X GPU cardcontaining 12 GB of memory, with 18% performance loss compared to ahypothetical, oracular GPU with enough memory to hold the entire DNN.

vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design