{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T16:48:04Z","timestamp":1765039684325,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031321795"},{"type":"electronic","value":"9783031321801"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-32180-1_8","type":"book-chapter","created":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T12:02:53Z","timestamp":1683806573000},"page":"114-133","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Towards a\u00a0Better 16-Bit Number Representation for\u00a0Training Neural Networks"],"prefix":"10.1007","author":[{"given":"Himeshi","family":"De Silva","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongshi","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nhut-Minh","family":"Ho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John L.","family":"Gustafson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weng-Fai","family":"Wong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,12]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Agrawal, A., et al.: DLFloat: a 16-bit floating point format designed for deep learning training and inference. In: 2019 IEEE 26th Symposium on Computer Arithmetic (ARITH), pp. 92\u201395. IEEE (2019)","DOI":"10.1109\/ARITH.2019.00023"},{"key":"8_CR2","doi-asserted-by":"crossref","unstructured":"Burgess, N., Milanovic, J., Stephens, N., Monachopoulos, K., Mansell, D.: BFloat16 processing for neural networks. In: 2019 IEEE 26th Symposium on Computer Arithmetic (ARITH), pp. 88\u201391. IEEE (2019)","DOI":"10.1109\/ARITH.2019.00022"},{"key":"8_CR3","unstructured":"IMS Committee: IEEE Standard for Floating-Point Arithmetic. IEEE Std. 754-2019 (2019)"},{"key":"8_CR4","unstructured":"Das, D., et al.: Mixed precision training of convolutional neural networks using integer operations. arXiv preprint arXiv:1802.00930 (2018)"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"De Silva, H., Gustafson, J.L., Wong, W.F.: Making Strassen matrix multiplication safe. In: 2018 IEEE 25th International Conference on High Performance Computing (HiPC), pp. 173\u2013182. IEEE (2018)","DOI":"10.1109\/HiPC.2018.00028"},{"key":"8_CR6","unstructured":"Gupta, S., Agrawal, A., Gopalakrishnan, K., Narayanan, P.: Deep learning with limited numerical precision. In: International Conference on Machine Learning, pp. 1737\u20131746 (2015)"},{"key":"8_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/978-3-031-09779-9_3","volume-title":"Next Generation Arithmetic","author":"NM Ho","year":"2022","unstructured":"Ho, N.M., De Silva, H., Gustafson, J.L., Wong, W.F.: Qtorch+: next generation arithmetic for Pytorch machine learning. In: Gustafson, J., Dimitrov, V. (eds.) CoNGA 2022. LNCS, pp. 31\u201349. Springer, Heidelberg (2022). https:\/\/doi.org\/10.1007\/978-3-031-09779-9_3"},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Ho, N.M., Nguyen, D.T., De Silva, H., Gustafson, J.L., Wong, W.F., Chang, I.J.: Posit arithmetic for the training and deployment of generative adversarial networks. In: 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1350\u20131355. IEEE (2021)","DOI":"10.23919\/DATE51398.2021.9473933"},{"key":"8_CR9","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and $$<$$0.5 mb model size. arXiv preprint arXiv:1602.07360 (2016)"},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675\u2013678. ACM (2014)","DOI":"10.1145\/2647868.2654889"},{"key":"8_CR11","unstructured":"Kalamkar, D., et al.: A study of bfloat16 for deep learning training. arXiv preprint arXiv:1905.12322 (2019)"},{"key":"8_CR12","unstructured":"K\u00f6ster, U., et al.: Flexpoint: an adaptive numerical format for efficient training of deep neural networks. In: Advances in Neural Information Processing Systems, pp. 1742\u20131752 (2017)"},{"key":"8_CR13","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. Citeseer (2009)"},{"key":"8_CR14","unstructured":"Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013)"},{"key":"8_CR15","doi-asserted-by":"crossref","unstructured":"Lu, J., et al.: Training deep neural networks using posit number system. arXiv preprint arXiv:1909.03831 (2019)","DOI":"10.1109\/SOCC46988.2019.1570558530"},{"key":"8_CR16","unstructured":"Mellempudi, N., Srinivasan, S., Das, D., Kaul, B.: Mixed precision training with 8-bit floating point. arXiv preprint arXiv:1905.12334 (2019)"},{"key":"8_CR17","unstructured":"Micikevicius, P., et al.: Mixed precision training. arXiv preprint arXiv:1710.03740 (2017)"},{"key":"8_CR18","doi-asserted-by":"crossref","unstructured":"Murillo, R., Del Barrio, A.A., Botella, G.: Deep PeNSieve: a deep learning framework based on the posit number system. Digit. Signal Process. 102762 (2020)","DOI":"10.1016\/j.dsp.2020.102762"},{"key":"8_CR19","unstructured":"Nvidia: Training mixed precision user guide (2020). https:\/\/docs.nvidia.com\/deeplearning\/sdk\/pdf\/Training-Mixed-Precision-User-Guide.pdf. Accessed 07 Mar 2020"},{"key":"8_CR20","unstructured":"Posit standard documentation (2022). https:\/\/posithub.org\/docs\/posit_standard-2.pdf. Accessed 07 Jan 2023"},{"issue":"3","key":"8_CR21","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vis."},{"key":"8_CR22","unstructured":"Sun, X., et al.: Hybrid 8-bit floating point (HFP8) training and inference for deep neural networks. In: Advances in Neural Information Processing Systems, pp. 4901\u20134910 (2019)"},{"key":"8_CR23","unstructured":"Sun, X., et al.: Ultra-low precision 4-bit training of deep neural networks. In: Advances in Neural Information Processing Systems, vol. 33 (2020)"},{"key":"8_CR24","unstructured":"Wang, N., Choi, J., Brand, D., Chen, C.Y., Gopalakrishnan, K.: Training deep neural networks with 8-bit floating point numbers. In: Advances in Neural Information Processing Systems, pp. 7675\u20137684 (2018)"},{"key":"8_CR25","unstructured":"Zhou, S., Wu, Y., Ni, Z., Zhou, X., Wen, H., Zou, Y.: DoReFa-net: training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv preprint arXiv:1606.06160 (2016)"}],"container-title":["Lecture Notes in Computer Science","Next Generation Arithmetic"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-32180-1_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T12:04:07Z","timestamp":1683806647000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-32180-1_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031321795","9783031321801"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-32180-1_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"12 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CoNGA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Conference on Next Generation Arithmetic","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 March 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 March 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"conga2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.sc-asia.org\/conga\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"11","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"69% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}