{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T16:15:05Z","timestamp":1781021705097,"version":"3.54.1"},"reference-count":61,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2025,6,15]],"date-time":"2025-06-15T00:00:00Z","timestamp":1749945600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,6,15]],"date-time":"2025-06-15T00:00:00Z","timestamp":1749945600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,15]],"date-time":"2025-06-15T00:00:00Z","timestamp":1749945600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Fundation","award":["2022YFB3902304"],"award-info":[{"award-number":["2022YFB3902304"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2025,6,15]]},"DOI":"10.1109\/jiot.2025.3543213","type":"journal-article","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T13:58:11Z","timestamp":1739973491000},"page":"20357-20376","source":"Crossref","is-referenced-by-count":16,"title":["High-Throughput and Energy-Efficient FPGA-Based Accelerator for All Adder Neural Networks"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4717-2304","authenticated-orcid":false,"given":"Ning","family":"Zhang","sequence":"first","affiliation":[{"name":"National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8155-8221","authenticated-orcid":false,"given":"Shuo","family":"Ni","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3273-5522","authenticated-orcid":false,"given":"Liang","family":"Chen","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tong","family":"Wang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4182-6493","authenticated-orcid":false,"given":"He","family":"Chen","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3144987"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2023.3239119"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3084396"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2915983"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2920283"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2019.04.058"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.04.138"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2022.3144874"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3390\/drones6070154"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3191717"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3163364"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2023.3241933"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2018.2884972"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/1498765.1498785"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2021.3060509"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11060945"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC.2014.6757323"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00154"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3267445"},{"key":"ref20","article-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size","author":"Iandola","year":"2016","journal-title":"arXiv:1602.07360"},{"key":"ref21","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017","journal-title":"arXiv:1704.04861"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3100063"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3119520"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2711426"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3034925"},{"key":"ref26","first-page":"5151","article-title":"Scalable methods for 8-bit training of neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Banner"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00345"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3124095"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00268"},{"key":"ref31","first-page":"2771","article-title":"ShiftAddNet: A hardware-inspired deep network","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"You"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.0030031"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO.2016.7783725"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ISPA\/IUCC.2017.00099"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3055814"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3508352.3549439"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2020.3002779"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2022.3178474"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3013637"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3116302"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3055240"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3128945"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2023.3279349"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3277869"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3179016"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2024.3409649"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3595633"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2024.3398215"},{"key":"ref49","article-title":"AdderNet and its minimalist hardware design for energyefficient artificial intelligence","author":"Wang","year":"2021","journal-title":"arXiv:2101.10015"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/1869790.1869829"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1080\/01431161.2011.608740"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2015.2475299"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2019.2941250"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/3174243.3174266"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2019.2899730"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2016.2616357"},{"key":"ref57","first-page":"8","article-title":"Xilinx 16nm ultrascale+ devices yield 2-5X performance\/watt advantage","volume":"90","author":"Santarini","year":"2015","journal-title":"XCell J."},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2023.3347417"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2024.3435996"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2021.101991"},{"key":"ref61","volume-title":"Convolutional neural network with INT4 optimization on Xilinx devices","author":"Han","year":"2020"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6488907\/11031138\/10896587.pdf?arnumber=10896587","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T21:10:00Z","timestamp":1771362600000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10896587\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,15]]},"references-count":61,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2025.3543213","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,15]]}}}