{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/vdnn-virtualized-deep-neural-networks-for","title":"vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design","arxiv_id":"1602.08124","date":"2016-02-25","proceeding":null,"authors":["Minsoo Rhu","Natalia Gimelshein","Jason Clemons","Arslan Zulfiqar","Stephen W. Keckler"],"abstract":"The most widely used machine learning frameworks require users to carefully\ntune their memory usage so that the deep neural network (DNN) fits into the\nDRAM capacity of a GPU. This restriction hampers a researcher's flexibility to\nstudy different machine learning algorithms, forcing them to either use a less\ndesirable network architecture or parallelize the processing across multiple\nGPUs. We propose a runtime memory manager that virtualizes the memory usage of\nDNNs such that both GPU and CPU memory can simultaneously be utilized for\ntraining larger DNNs. Our virtualized DNN (vDNN) reduces the average GPU memory\nusage of AlexNet by up to 89%, OverFeat by 91%, and GoogLeNet by 95%, a\nsignificant reduction in memory requirements of DNNs. Similar experiments on\nVGG-16, one of the deepest and memory hungry DNNs to date, demonstrate the\nmemory-efficiency of our proposal. vDNN enables VGG-16 with batch size 256\n(requiring 28 GB of memory) to be trained on a single NVIDIA Titan X GPU card\ncontaining 12 GB of memory, with 18% performance loss compared to a\nhypothetical, oracular GPU with enough memory to hold the entire DNN.","url_abs":"http://arxiv.org/abs/1602.08124v3","url_pdf":"http://arxiv.org/pdf/1602.08124v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"vdnn-virtualized-deep-neural-networks-for","repo_url":"https://github.com/adderbyte/ImageSegmentationTensorflow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"vdnn-virtualized-deep-neural-networks-for","repo_url":"https://github.com/adderbyte/MultiClassLabelSegTF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"vdnn-virtualized-deep-neural-networks-for","repo_url":"https://github.com/adderbyte/MultiClassSegmentationTensorflow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"vdnn-virtualized-deep-neural-networks-for","repo_url":"https://github.com/shriramsb/vdnn-plus-plus","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":null,"task_name":"CPU"},{"task_slug":"efficient-neural-network","task_name":"Efficient Neural Network"},{"task_slug":null,"task_name":"GPU"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"auxiliary-classifier","method_name":"Auxiliary Classifier"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"googlenet","method_name":"GoogLeNet"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"inception-module","method_name":"Inception Module"},{"method_slug":"local-response-normalization","method_name":"Local Response Normalization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"overfeat","method_name":"OverFeat"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1602.08124","atlas_url":"https://app.syntology.ai/?focus=1602.08124","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}