{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T09:29:10Z","timestamp":1766136550050,"version":"3.41.0"},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2015,1,9]],"date-time":"2015-01-09T00:00:00Z","timestamp":1420761600000},"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. Archit. Code Optim."],"published-print":{"date-parts":[[2015,1,9]]},"abstract":"<jats:p>\n            This article presents an approximate data encoding scheme called\n            <jats:italic>Significant Position Encoding (SPE)<\/jats:italic>\n            . The encoding allows efficient implementation of the recall phase (forward propagation pass) of Convolutional Neural Networks (CNN)\u2014a typical Feed-Forward Neural Network. This implementation uses only 7 bits data representation and achieves almost the same classification performance compared with the initial network: on MNIST handwriting recognition task, using this data encoding scheme losses only 0.03% in terms of recognition rate (99.27% vs. 99.3%). In terms of storage, we achieve a 12.5% gain compared with an 8 bits fixed-point implementation of the same CNN. Moreover, this data encoding allows efficient implementation of processing unit thanks to the simplicity of scalar product operation\u2014the principal operation in a Feed-Forward Neural Network.\n          <\/jats:p>","DOI":"10.1145\/2685394","type":"journal-article","created":{"date-parts":[[2015,1,12]],"date-time":"2015-01-12T20:02:10Z","timestamp":1421092930000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Efficient Data Encoding for Convolutional Neural Network application"],"prefix":"10.1145","volume":"11","author":[{"given":"Hong-Phuc","family":"Trinh","sequence":"first","affiliation":[{"name":"CEA, LIST, Embedded Computing Laboratory, France"}]},{"given":"Marc","family":"Duranton","sequence":"additional","affiliation":[{"name":"CEA, LIST, Embedded Computing Laboratory, France"}]},{"given":"Michel","family":"Paindavoine","sequence":"additional","affiliation":[{"name":"LEAD, UMR University of Bourgogne - CNRS, France"}]}],"member":"320","published-online":{"date-parts":[[2015,1,9]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/12.238495"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEC.1961.5219227"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2485922.2485923"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2003.816362"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2541940.2541967"},{"volume-title":"Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. 17--21","author":"Chen Z.","key":"e_1_2_1_6_1","unstructured":"Z. Chen , S. Haykin , and S. Becker . 2004. Theory of monte carlo sampling-based alopex algorithms for neural networks . In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. 17--21 . Z. Chen, S. Haykin, and S. Becker. 2004. Theory of monte carlo sampling-based alopex algorithms for neural networks. In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. 17--21."},{"volume-title":"Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201912)","author":"Ciresan D.","key":"e_1_2_1_7_1","unstructured":"D. Ciresan , U. Meier , and J. Schmidhuber . 2012. Multi-column deep neural networks for image classification . In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201912) . IEEE, 3642--3649. D. Ciresan, U. Meier, and J. Schmidhuber. 2012. Multi-column deep neural networks for image classification. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201912). IEEE, 3642--3649."},{"volume-title":"Proceedings of the 4th International Conference on Microelectronics for Neural Networks and Fuzzy Systems. IEEE, 46--55","author":"Cloutier J.","key":"e_1_2_1_8_1","unstructured":"J. Cloutier and P. Y. Simard . 1994. Hardware implementation of the backpropagation without multiplication . In Proceedings of the 4th International Conference on Microelectronics for Neural Networks and Fuzzy Systems. IEEE, 46--55 . J. Cloutier and P. Y. Simard. 1994. Hardware implementation of the backpropagation without multiplication. In Proceedings of the 4th International Conference on Microelectronics for Neural Networks and Fuzzy Systems. IEEE, 46--55."},{"volume-title":"Proceedings of the Conference on Information Sciences and Systems.","author":"Coleman J. O.","key":"e_1_2_1_9_1","unstructured":"J. O. Coleman and A. Yurdakul . 2001. Fractions in the canonical-signed-digit number system . In Proceedings of the Conference on Information Sciences and Systems. J. O. Coleman and A. Yurdakul. 2001. Fractions in the canonical-signed-digit number system. In Proceedings of the Conference on Information Sciences and Systems."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065700000181"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO.2012.48"},{"volume-title":"Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS\u201910)","author":"Farabet C.","key":"e_1_2_1_12_1","unstructured":"C. Farabet , B. Martini , P. Akselrod , S. Talay , Y. LeCun , and E. Culurciello . 2010. Hardware accelerated convolutional neural networks for synthetic vision systems . In Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS\u201910) . IEEE, 257--260. C. Farabet, B. Martini, P. Akselrod, S. Talay, Y. LeCun, and E. Culurciello. 2010. Hardware accelerated convolutional neural networks for synthetic vision systems. In Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS\u201910). IEEE, 257--260."},{"volume-title":"Advances in Information Systems Science","author":"Gaines B. R.","key":"e_1_2_1_13_1","unstructured":"B. R. Gaines . 1969. Stochastic computing systems . In Advances in Information Systems Science . Springer , 37--172. B. R. Gaines. 1969. Stochastic computing systems. In Advances in Information Systems Science. Springer, 37--172."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90020-8"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.105429"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2010.03.021"},{"key":"e_1_2_1_17_1","first-page":"6","article-title":"A programmable FIR digital filter using CSD coefficients","volume":"31","author":"Khoo K. Y.","year":"1996","unstructured":"K. Y. Khoo , A. Kwentus , and A. N. Willson Jr . 1996 . A programmable FIR digital filter using CSD coefficients . IEEE J. Solid-State Circuits 31 , 6 (June 1996), 869--874. DOI: http:\/\/dx.doi.org\/10.1109\/4.509877 10.1109\/4.509877 K. Y. Khoo, A. Kwentus, and A. N. Willson Jr. 1996. A programmable FIR digital filter using CSD coefficients. IEEE J. Solid-State Circuits 31, 6 (June 1996), 869--874. DOI: http:\/\/dx.doi.org\/10.1109\/4.509877","journal-title":"IEEE J. Solid-State Circuits"},{"key":"e_1_2_1_18_1","first-page":"4","article-title":"ImageNet classification with deep convolutional neural networks","volume":"1","author":"Krizhevsky A.","year":"2012","unstructured":"A. Krizhevsky , I. Sutskever , and G. E. Hinton . 2012 . ImageNet classification with deep convolutional neural networks . In NIPS , Vol. 1. 4 . A. Krizhevsky, I. Sutskever, and G. E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. In NIPS, Vol. 1. 4.","journal-title":"NIPS"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"volume-title":"Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS\u201910)","author":"LeCun Y.","key":"e_1_2_1_20_1","unstructured":"Y. LeCun , K. Kavukcuoglu , and C. Farabet . 2010. Convolutional networks and applications in vision . In Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS\u201910) . IEEE, 253--256. Y. LeCun, K. Kavukcuoglu, and C. Farabet. 2010. Convolutional networks and applications in vision. In Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS\u201910). IEEE, 253--256."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2011.09.039"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.129414"},{"key":"e_1_2_1_23_1","first-page":"2","article-title":"Neural network adaptations to hardware implementations","volume":"1","author":"Moerland P.","year":"1997","unstructured":"P. Moerland and E. Fiesler . 1997 . Neural network adaptations to hardware implementations . Handbook of Neural Computation 1 (1997), 2 . P. Moerland and E. Fiesler. 1997. Neural network adaptations to hardware implementations. Handbook of Neural Computation 1 (1997), 2.","journal-title":"Handbook of Neural Computation"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-007-0086-x"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASSP.1976.1162774"},{"volume-title":"Proceedings of the 1990 International Joint Conference on Neural Networks (IJCNN\u201990)","author":"Sequin C. H.","key":"e_1_2_1_26_1","unstructured":"C. H. Sequin and R. D. Clay . 1990. Fault tolerance in artificial neural networks . In Proceedings of the 1990 International Joint Conference on Neural Networks (IJCNN\u201990) . IEEE, 703--708. C. H. Sequin and R. D. Clay. 1990. Fault tolerance in artificial neural networks. In Proceedings of the 1990 International Joint Conference on Neural Networks (IJCNN\u201990). IEEE, 703--708."},{"key":"e_1_2_1_27_1","volume-title":"Proceedings of the 7th International Conference on Document Analysis and Recognition. 958--963","author":"Simard P. Y.","year":"2003","unstructured":"P. Y. Simard , D. Steinkraus , and J. C. Platt . 2003. Best practices for convolutional neural networks applied to visual document analysis . In Proceedings of the 7th International Conference on Document Analysis and Recognition. 958--963 . DOI: http:\/\/dx.doi.org\/10.1109\/ICDAR. 2003 .1227801 10.1109\/ICDAR.2003.1227801 P. Y. Simard, D. Steinkraus, and J. C. Platt. 2003. Best practices for convolutional neural networks applied to visual document analysis. In Proceedings of the 7th International Conference on Document Analysis and Recognition. 958--963. DOI: http:\/\/dx.doi.org\/10.1109\/ICDAR.2003.1227801"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2008.919386"},{"key":"e_1_2_1_29_1","first-page":"375","article-title":"Fast neural network implementation","volume":"9","author":"Skrbek M.","year":"1999","unstructured":"M. Skrbek . 1999 . Fast neural network implementation . Neural Network World 9 , 5 (1999), 375 -- 391 . M. Skrbek. 1999. Fast neural network implementation. Neural Network World 9, 5 (1999), 375--391.","journal-title":"Neural Network World"},{"key":"e_1_2_1_30_1","volume-title":"Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN\u201900)","volume":"2","author":"Szab\u00f3 T.","unstructured":"T. Szab\u00f3 , L. Antoni , G. Horv\u00e1th , and B. Feh\u00e9r . 2000. A full-parallel digital implementation for pre-trained NNs . In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN\u201900) , Vol. 2 . IEEE, 49--54. T. Szab\u00f3, L. Antoni, G. Horv\u00e1th, and B. Feh\u00e9r. 2000. A full-parallel digital implementation for pre-trained NNs. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN\u201900), Vol. 2. IEEE, 49--54."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2366231.2337200"},{"volume-title":"Proceedings of the 30th International Conference on Machine Learning (ICML\u201913)","author":"Wan L.","key":"e_1_2_1_32_1","unstructured":"L. Wan , M. Zeiler , S. Zhang , Y. LeCun , and R. Fergus . 2013. Regularization of neural networks using dropconnect . In Proceedings of the 30th International Conference on Machine Learning (ICML\u201913) . 1058--1066. L. Wan, M. Zeiler, S. Zhang, Y. LeCun, and R. Fergus. 2013. Regularization of neural networks using dropconnect. In Proceedings of the 30th International Conference on Machine Learning (ICML\u201913). 1058--1066."}],"container-title":["ACM Transactions on Architecture and Code Optimization"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2685394","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2685394","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T06:13:44Z","timestamp":1750227224000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2685394"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,1,9]]},"references-count":32,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2015,1,9]]}},"alternative-id":["10.1145\/2685394"],"URL":"https:\/\/doi.org\/10.1145\/2685394","relation":{},"ISSN":["1544-3566","1544-3973"],"issn-type":[{"type":"print","value":"1544-3566"},{"type":"electronic","value":"1544-3973"}],"subject":[],"published":{"date-parts":[[2015,1,9]]},"assertion":[{"value":"2014-06-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2014-10-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2015-01-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}