{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T12:56:35Z","timestamp":1773406595266,"version":"3.50.1"},"reference-count":21,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2014,2,1]],"date-time":"2014-02-01T00:00:00Z","timestamp":1391212800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Reconfigurable Technol. Syst."],"published-print":{"date-parts":[[2014,2]]},"abstract":"<jats:p>Artificial neural networks (ANNs) are a natural target for hardware acceleration by FPGAs and GPGPUs because commercial-scale applications can require days to weeks to train using CPUs, and the algorithms are highly parallelizable. Previous work on FPGAs has shown how hardware parallelism can be used to accelerate a \u201cRestricted Boltzmann Machine\u201d (RBM) ANN algorithm, and how to distribute computation across multiple FPGAs.<\/jats:p>\n          <jats:p>Here we describe a fully pipelined parallel architecture that exploits \u201cmini-batch\u201d training (combining many input cases to compute each set of weight updates) to further accelerate ANN training. We implement on an FPGA, for the first time to our knowledge, a more powerful variant of the basic RBM, the \u201cFactored RBM\u201d (fRBM). The fRBM has proved valuable in learning transformations and in discovering features that are present across multiple types of input. We obtain (in simulation) a 100-fold acceleration (vs. CPU software) for an fRBM having N = 256 units in each of its four groups (two input, one output, one intermediate group of units) running on a Virtex-6 LX760 FPGA. Many of the architectural features we implement are applicable not only to fRBMs, but to basic RBMs and other ANN algorithms more broadly.<\/jats:p>","DOI":"10.1145\/2539125","type":"journal-article","created":{"date-parts":[[2014,2,26]],"date-time":"2014-02-26T13:07:08Z","timestamp":1393420028000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["A Fully Pipelined FPGA Architecture of a Factored Restricted Boltzmann Machine Artificial Neural Network"],"prefix":"10.1145","volume":"7","author":[{"given":"Lok-Won","family":"Kim","sequence":"first","affiliation":[{"name":"Cisco Systems"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sameh","family":"Asaad","sequence":"additional","affiliation":[{"name":"IBM T. J. Watson Research Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ralph","family":"Linsker","sequence":"additional","affiliation":[{"name":"IBM T. J. Watson Research Center"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2014,2]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1049\/ip-cds:19971587"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/4.104196"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2004.08.011"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the International Joint Conference on Neural Networks. 121--126","author":"Holt J.","unstructured":"Holt , J. and Baker , T . 1991. Back propagation simulations using limited precision calculations . In Proceedings of the International Joint Conference on Neural Networks. 121--126 . Holt, J. and Baker, T. 1991. Back propagation simulations using limited precision calculations. In Proceedings of the International Joint Conference on Neural Networks. 121--126."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/12.210171"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2006.888791"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/FCCM.2010.38"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/11760191_192"},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the 3rd Workshop on Neural Networks. 26--30","author":"Lindsey C.","unstructured":"Lindsey , C. and Lindblad , T . 1994. Review of hardware neural networks: A user\u2019s perspective . In Proceedings of the 3rd Workshop on Neural Networks. 26--30 . Lindsey, C. and Lindblad, T. 1994. Review of hardware neural networks: A user\u2019s perspective. In Proceedings of the 3rd Workshop on Neural Networks. 26--30."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2073481"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2003.811357"},{"key":"e_1_2_1_13_1","volume-title":"Proceedings of the Symposium on Computer Vision and Pattern Recognition (CVPR\u201907)","author":"Memisevic R.","unstructured":"Memisevic , R. and Hinton , G . 2007. Unsupervised learning of image transformations . In Proceedings of the Symposium on Computer Vision and Pattern Recognition (CVPR\u201907) . Memisevic, R. and Hinton, G. 2007. Unsupervised learning of image transformations. In Proceedings of the Symposium on Computer Vision and Pattern Recognition (CVPR\u201907)."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2010.01-09-953"},{"key":"e_1_2_1_15_1","first-page":"1603","article-title":"Gated softmax classification","volume":"23","author":"Memisevic R.","year":"2010","unstructured":"Memisevic , R. , Zach , C. , Hinton , G. , and Pollefeys , M. 2010 . Gated softmax classification . Neural Inf. Proc. Syst. 23 , 1603 -- 1611 . Memisevic, R., Zach, C., Hinton, G., and Pollefeys, M. 2010. Gated softmax classification. Neural Inf. Proc. Syst. 23, 1603--1611.","journal-title":"Neural Inf. Proc. Syst."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2004.01.013"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553486"},{"key":"e_1_2_1_18_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2551--2558","author":"Ranzato M.","unstructured":"Ranzato , M. and Hinton , G. E . 2010. Modeling pixel means and covariances using factorized third-order Boltzmann machines . In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2551--2558 . Ranzato, M. and Hinton, G. E. 2010. Modeling pixel means and covariances using factorized third-order Boltzmann machines. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2551--2558."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1090\/S0025-5718-1965-0184406-1"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553505"},{"key":"e_1_2_1_21_1","volume-title":"Proceedings of the 13th International Conference on Field-Programmable Logic and Applications. 1062--1066","author":"Zhu J.","unstructured":"Zhu , J. and Sutton , P . 2003. FPGA implementations of neural networks - A survey of a decade of progress . In Proceedings of the 13th International Conference on Field-Programmable Logic and Applications. 1062--1066 . Zhu, J. and Sutton, P. 2003. FPGA implementations of neural networks - A survey of a decade of progress. In Proceedings of the 13th International Conference on Field-Programmable Logic and Applications. 1062--1066."}],"container-title":["ACM Transactions on Reconfigurable Technology and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2539125","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2539125","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T07:34:50Z","timestamp":1750232090000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2539125"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,2]]},"references-count":21,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2014,2]]}},"alternative-id":["10.1145\/2539125"],"URL":"https:\/\/doi.org\/10.1145\/2539125","relation":{},"ISSN":["1936-7406","1936-7414"],"issn-type":[{"value":"1936-7406","type":"print"},{"value":"1936-7414","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,2]]},"assertion":[{"value":"2013-02-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2013-09-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2014-02-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}