{"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/firecaffe-near-linear-acceleration-of-deep","title":"FireCaffe: near-linear acceleration of deep neural network training on compute clusters","arxiv_id":"1511.00175","date":"2015-10-31","proceeding":"CVPR 2016 6","authors":["Forrest N. Iandola","Khalid Ashraf","Matthew W. Moskewicz","Kurt Keutzer"],"abstract":"Long training times for high-accuracy deep neural networks (DNNs) impede\nresearch into new DNN architectures and slow the development of high-accuracy\nDNNs. In this paper we present FireCaffe, which successfully scales deep neural\nnetwork training across a cluster of GPUs. We also present a number of best\npractices to aid in comparing advancements in methods for scaling and\naccelerating the training of deep neural networks. The speed and scalability of\ndistributed algorithms is almost always limited by the overhead of\ncommunicating between servers; DNN training is not an exception to this rule.\nTherefore, the key consideration here is to reduce communication overhead\nwherever possible, while not degrading the accuracy of the DNN models that we\ntrain. Our approach has three key pillars. First, we select network hardware\nthat achieves high bandwidth between GPU servers -- Infiniband or Cray\ninterconnects are ideal for this. Second, we consider a number of communication\nalgorithms, and we find that reduction trees are more efficient and scalable\nthan the traditional parameter server approach. Third, we optionally increase\nthe batch size to reduce the total quantity of communication during DNN\ntraining, and we identify hyperparameters that allow us to reproduce the\nsmall-batch accuracy while training with large batch sizes. When training\nGoogLeNet and Network-in-Network on ImageNet, we achieve a 47x and 39x speedup,\nrespectively, when training on a cluster of 128 GPUs.","url_abs":"http://arxiv.org/abs/1511.00175v2","url_pdf":"http://arxiv.org/pdf/1511.00175v2.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":[],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"image-classification","task_name":"Image Classification"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-imagenet","task":"Image Classification","dataset":"ImageNet","model":"FireCaffe (GoogLeNet)","rank_in_archive_order":1033,"of":1060,"metrics":{"Top 1 Accuracy":"68.3%"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-imagenet","task":"Image Classification","dataset":"ImageNet","model":"FireCaffe (AlexNet)","rank_in_archive_order":1053,"of":1060,"metrics":{"Top 1 Accuracy":"58.9%"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1511.00175","atlas_url":"https://app.syntology.ai/?focus=1511.00175","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}