{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T15:41:17Z","timestamp":1780674077474,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":13,"publisher":"ACM","license":[{"start":{"date-parts":[[2015,5,31]],"date-time":"2015-05-31T00:00:00Z","timestamp":1433030400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000185","name":"Defense Advanced Research Projects Agency","doi-asserted-by":"publisher","award":["FA8750-12-2-0335, FA8750-13-2-0039"],"award-info":[{"award-number":["FA8750-12-2-0335, FA8750-13-2-0039"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1353606"],"award-info":[{"award-number":["IIS-1353606"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U54EB020405"],"award-info":[{"award-number":["U54EB020405"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["N000141210041, N000141310129"],"award-info":[{"award-number":["N000141210041, N000141310129"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2015,5,31]]},"DOI":"10.1145\/2799562.2799641","type":"proceedings-article","created":{"date-parts":[[2015,7,27]],"date-time":"2015-07-27T14:35:23Z","timestamp":1438007723000},"page":"1-4","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":33,"title":["Caffe con Troll"],"prefix":"10.1145","author":[{"given":"Stefan","family":"Hadjis","sequence":"first","affiliation":[{"name":"Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Firas","family":"Abuzaid","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ce","family":"Zhang","sequence":"additional","affiliation":[{"name":"Stanford University and University of Wisconsin-Madison"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"R\u00e9","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2015,5,31]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.25080\/Majora-92bf1922-003"},{"key":"e_1_3_2_1_2_1","volume-title":"ICFHR","author":"Chellapilla K.","year":"2006","unstructured":"K. Chellapilla High performance convolutional neural networks for document processing . ICFHR , 2006 . K. Chellapilla et al. High performance convolutional neural networks for document processing. ICFHR, 2006."},{"key":"e_1_3_2_1_3_1","volume-title":"cuDNN: Efficient Primitives for Deep Learning. ArXiv e-prints","author":"Chetlur S.","year":"2014","unstructured":"S. Chetlur , C. Woolley , P. Vandermersch , J. Cohen , J. Tran , B. Catanzaro , and E. Shelhamer . cuDNN: Efficient Primitives for Deep Learning. ArXiv e-prints , 2014 . S. Chetlur, C. Woolley, P. Vandermersch, J. Cohen, J. Tran, B. Catanzaro, and E. Shelhamer. cuDNN: Efficient Primitives for Deep Learning. ArXiv e-prints, 2014."},{"key":"e_1_3_2_1_4_1","volume-title":"OSDI","author":"Chilimbi T.","year":"2014","unstructured":"T. Chilimbi , Y. Suzue , J. Apacible , and K. Kalyanaraman . Project adam: Building an efficient and scalable deep learning training system . In OSDI , 2014 . T. Chilimbi, Y. Suzue, J. Apacible, and K. Kalyanaraman. Project adam: Building an efficient and scalable deep learning training system. In OSDI, 2014."},{"key":"e_1_3_2_1_5_1","volume-title":"NIPS","author":"Dean J.","year":"2012","unstructured":"J. Dean Large scale distributed deep networks . In NIPS , 2012 . J. Dean et al. Large scale distributed deep networks. In NIPS, 2012."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1561\/2000000039"},{"key":"e_1_3_2_1_7_1","volume-title":"Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093","author":"Jia Y.","year":"2014","unstructured":"Y. Jia , E. Shelhamer , J. Donahue , S. Karayev , J. Long , R. Girshick , S. Guadarrama , and T. Darrell . Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 , 2014 . Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093, 2014."},{"key":"e_1_3_2_1_8_1","volume-title":"NIPS","author":"Krizhevsky A.","year":"2012","unstructured":"A. Krizhevsky , I. Sutskever , and G. E. Hinton . ImageNet classification with deep convolutional neural networks . In NIPS , 2012 . A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, 2012."},{"key":"e_1_3_2_1_9_1","first-page":"693","volume-title":"NIPS","author":"Niu F.","year":"2011","unstructured":"F. Niu , B. Recht , C. R\u00e9 , and S. J. Wright . Hogwild!: A lock-free approach to parallelizing stochastic gradient descent . In NIPS , pages 693 -- 701 , 2011 . F. Niu, B. Recht, C. R\u00e9, and S. J. Wright. Hogwild!: A lock-free approach to parallelizing stochastic gradient descent. In NIPS, pages 693--701, 2011."},{"key":"e_1_3_2_1_10_1","volume-title":"NIPS workshop on Distributed Machine Learning and Matrix Computations","author":"Noel C.","year":"2014","unstructured":"C. Noel and S. Osindero . Dogwild! --- Distributed Hogwild for CPU & GPU . In NIPS workshop on Distributed Machine Learning and Matrix Computations , 2014 . C. Noel and S. Osindero. Dogwild! --- Distributed Hogwild for CPU & GPU. In NIPS workshop on Distributed Machine Learning and Matrix Computations, 2014."},{"key":"e_1_3_2_1_11_1","volume-title":"Dec.","author":"Vasilache N.","year":"2014","unstructured":"N. Vasilache , J. Johnson , M. Mathieu , S. Chintala , S. Piantino , and Y. LeCun . Fast Convolutional Nets With fbfft: A GPU Performance Evaluation. ArXiv e-prints , Dec. 2014 . N. Vasilache, J. Johnson, M. Mathieu, S. Chintala, S. Piantino, and Y. LeCun. Fast Convolutional Nets With fbfft: A GPU Performance Evaluation. ArXiv e-prints, Dec. 2014."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732977.2733001"},{"key":"e_1_3_2_1_14_1","volume-title":"Text Understanding from Scratch. ArXiv e-prints","author":"Zhang X.","year":"2015","unstructured":"X. Zhang and Y. LeCun . Text Understanding from Scratch. ArXiv e-prints , 2015 . X. Zhang and Y. LeCun. Text Understanding from Scratch. ArXiv e-prints, 2015."}],"event":{"name":"SIGMOD\/PODS'15: International Conference on Management of Data","location":"Melbourne VIC Australia","acronym":"SIGMOD\/PODS'15","sponsor":["SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the Fourth Workshop on Data analytics in the Cloud"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2799562.2799641","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2799562.2799641","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T05:42:42Z","timestamp":1750225362000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2799562.2799641"}},"subtitle":["Shallow Ideas to Speed Up Deep Learning"],"short-title":[],"issued":{"date-parts":[[2015,5,31]]},"references-count":13,"alternative-id":["10.1145\/2799562.2799641","10.1145\/2799562"],"URL":"https:\/\/doi.org\/10.1145\/2799562.2799641","relation":{},"subject":[],"published":{"date-parts":[[2015,5,31]]},"assertion":[{"value":"2015-05-31","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}