{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T15:58:50Z","timestamp":1765209530805,"version":"3.46.0"},"publisher-location":"New York, NY, USA","reference-count":15,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T00:00:00Z","timestamp":1700179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 92164203, 62334006"],"award-info":[{"award-number":["No. 92164203, 62334006"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Program of Xinjiang Uygur Autonomous Region","award":["No.2022B01008"],"award-info":[{"award-number":["No.2022B01008"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,11,17]]},"DOI":"10.1145\/3652628.3652788","type":"proceedings-article","created":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T10:36:46Z","timestamp":1716460606000},"page":"963-968","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["\"\\\"Peak\\\" Quantization: A Training Method Suitable for Terminal Equipment to Deploy Keyword Spotting Network\""],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8258-0251","authenticated-orcid":false,"given":"Xiaomeng","family":"Luo","sequence":"first","affiliation":[{"name":"College of Intelligent Equipment, Shandong University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5461-8901","authenticated-orcid":false,"given":"Guangcun","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Intelligent Equipment, Shandong University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7400-8900","authenticated-orcid":false,"given":"Yuhao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Beihang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2207-6092","authenticated-orcid":false,"given":"Xiaotao","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Integrated Circuit Science and Engineering, Beihang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5882-0779","authenticated-orcid":false,"given":"Xinghua","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Science, Beijing Forestry University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6946-592X","authenticated-orcid":false,"given":"Junlin","family":"Li","sequence":"additional","affiliation":[{"name":"No.208 Research Institute of China Ordnance Industries, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3189-7562","authenticated-orcid":false,"given":"Qi","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Precision Instrument, Tsinghua University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5054-9590","authenticated-orcid":false,"given":"Fei","family":"Qiao","sequence":"additional","affiliation":[{"name":"Department of Clectronic Cngineering, Tsinghua University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,5,23]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Chun Yang Ruiyao Zhang Long Huang Shutong Ti Jinhui Lin Zhiwei Dong and Xucheng Yin. 2023. Review of the quantitative method of deep neural network model.Journal of Engineering Science(12)."},{"key":"e_1_3_2_1_2_1","unstructured":"Das D Mellempudi N Mudigere D Kalamkar D Avancha S Banerjee K ... and Pirogov V. 2018. Mixed precision training of convolutional neural networks using integer operations. arXiv preprint arXiv:1802.00930."},{"volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778)","author":"He K","key":"e_1_3_2_1_3_1","unstructured":"He K, Zhang X, Ren S, and Sun J. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778)."},{"key":"e_1_3_2_1_4_1","unstructured":"Xu C Yao J Lin Z Ou W Cao Y Wang Z and Zha H. 2018. Alternating multi-bit quantization for recurrent neural networks. arXiv preprint arXiv:1802.00150."},{"volume-title":"European conference on computer vision (pp. 525-542)","author":"Rastegari M","key":"e_1_3_2_1_5_1","unstructured":"Rastegari M, Ordonez V, Redmon J, and Farhadi A. 2016. Xnor-net: Imagenet classification using binary convolutional neural networks. In European conference on computer vision (pp. 525-542). Cham: Springer International Publishing."},{"key":"e_1_3_2_1_6_1","volume-title":"Binaryconnect: Training deep neural networks with binary weights during propagations. Advances in neural information processing systems, 28.","author":"Courbariaux M","year":"2015","unstructured":"Courbariaux M, Bengio Y, and David J P. 2015. Binaryconnect: Training deep neural networks with binary weights during propagations. Advances in neural information processing systems, 28."},{"volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 4942-4952)","author":"Liu Z","key":"e_1_3_2_1_7_1","unstructured":"Liu Z, Cheng K T, Huang D, Xing E P, and Shen Z. 2022. Nonuniform-to-uniform quantization: Towards accurate quantization via generalized straight-through estimation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 4942-4952)."},{"key":"e_1_3_2_1_8_1","unstructured":"Lin S Ma X Ye S Yuan G Ma K and Wang Y. 2019. Toward extremely low bit and lossless accuracy in dnns with progressive admm. arXiv preprint arXiv:1905.00789."},{"key":"e_1_3_2_1_9_1","unstructured":"Krishnamoorthi R. 2018. Quantizing deep convolutional networks for efficient inference: A whitepaper. arXiv preprint arXiv:1806.08342."},{"key":"e_1_3_2_1_10_1","unstructured":"Weisstein E W. 2002. Normal distribution. https:\/\/mathworld. wolfram. com\/."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2979799"},{"volume-title":"ESSCIRC 2018-IEEE 44th European Solid State Circuits Conference (ESSCIRC) (pp. 166-169)","author":"Giraldo J S P","key":"e_1_3_2_1_12_1","unstructured":"Giraldo J S P, and Verhelst M. 2018. Laika: A 5uW programmable LSTM accelerator for always-on keyword spotting in 65nm CMOS. In ESSCIRC 2018-IEEE 44th European Solid State Circuits Conference (ESSCIRC) (pp. 166-169). IEEE."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2022.3142525"},{"volume-title":"ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.","author":"Macha S","key":"e_1_3_2_1_14_1","unstructured":"Macha S, Oza O, Escott A, Caliva F, Armitano R, Cheekatmalla S K, ... and Liu Y. 2023. Fixed-point quantization aware training for on-device keyword-spotting. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE."},{"volume-title":"2020 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). IEEE.","author":"Liu B","key":"e_1_3_2_1_15_1","unstructured":"Liu B, Cai H, Gong Y, Zhu W, Li Y, Ge W, and Wang Z. 2020. Binarized weight neural-network inspired ultra-low power speech recognition processor with time-domain based digital-analog mixed approximate computing. In 2020 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). IEEE."}],"event":{"name":"ICAICE 2023: The 4th International Conference on Artificial Intelligence and Computer Engineering","acronym":"ICAICE 2023","location":"Dalian China"},"container-title":["Proceedings of the 4th International Conference on Artificial Intelligence and Computer Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652628.3652788","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3652628.3652788","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T15:30:13Z","timestamp":1765207813000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652628.3652788"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,17]]},"references-count":15,"alternative-id":["10.1145\/3652628.3652788","10.1145\/3652628"],"URL":"https:\/\/doi.org\/10.1145\/3652628.3652788","relation":{},"subject":[],"published":{"date-parts":[[2023,11,17]]},"assertion":[{"value":"2024-05-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}