{"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/caffe-con-troll-shallow-ideas-to-speed-up","title":"Caffe con Troll: Shallow Ideas to Speed Up Deep Learning","arxiv_id":"1504.04343","date":"2015-04-16","proceeding":null,"authors":["Stefan Hadjis","Firas Abuzaid","Ce Zhang","Christopher Ré"],"abstract":"We present Caffe con Troll (CcT), a fully compatible end-to-end version of\nthe popular framework Caffe with rebuilt internals. We built CcT to examine the\nperformance characteristics of training and deploying general-purpose\nconvolutional neural networks across different hardware architectures. We find\nthat, by employing standard batching optimizations for CPU training, we achieve\na 4.5x throughput improvement over Caffe on popular networks like CaffeNet.\nMoreover, with these improvements, the end-to-end training time for CNNs is\ndirectly proportional to the FLOPS delivered by the CPU, which enables us to\nefficiently train hybrid CPU-GPU systems for CNNs.","url_abs":"http://arxiv.org/abs/1504.04343v2","url_pdf":"http://arxiv.org/pdf/1504.04343v2.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":"caffe-con-troll-shallow-ideas-to-speed-up","repo_url":"https://github.com/HazyResearch/CaffeConTroll","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":null,"task_name":"GPU"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}