{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:47:35Z","timestamp":1787017655807,"version":"build-2736575974"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T00:00:00Z","timestamp":1723161600000},"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":[],"published-print":{"date-parts":[[2024,8,9]]},"DOI":"10.1145\/3697467.3697591","type":"proceedings-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T16:31:52Z","timestamp":1731083512000},"page":"34-42","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A Review of FPGA Accelerated Computing Methods for YOLO Models"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6838-060X","authenticated-orcid":false,"given":"Yifan","family":"Bian","sequence":"first","affiliation":[{"name":"School of Semiconductor Science and Technology, South China Normal University, Foshan, Guangdong, China, Foshan, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9411-103X","authenticated-orcid":false,"given":"Dongxiang","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou, Guangdong, China, Guangzhou, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0011-1379","authenticated-orcid":false,"given":"Menglong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Semiconductor Science and Technology, South China Normal University, Foshan, Guangdong, China, Foshan, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"e_1_3_3_1_1_2","first-page":"36","volume":"2018","author":"Molanes R. F.","unstructured":"Molanes, R. F.; Amarasinghe, K., Rodriguez-Andina, J.; Manic, M. Deep learning and reconfigurable platforms in the internet of things: Challenges and opportunities in algorithms and hardware. IEEE Ind. Electron 2018, 12, 36-49.","journal-title":"IEEE Ind. Electron"},{"key":"e_1_3_3_1_3_2","first-page":"185","volume-title":"Seaside CA, United States, 24 February","author":"Attia S.","year":"2020","unstructured":"Attia, S.; Betz, V. StateMover: Combining simulation and hardware execution for efficient FPGA debugging. FPGA '20: Proceedings of the 2020 ACM\/SIGDA InternationalF Symposium on Field-Programmable Gate Arrays, Seaside CA, United States, 24 February; Association for Computer Machinery: New York, United States, 2020; pp. 175\u2013185."},{"key":"e_1_3_3_1_4_2","volume-title":"2020 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), Sydney NSW, Australia, 21-24 April; IEEE: New York, United States","author":"Geier M.","unstructured":"Geier, M.; Br\u00e4ndle, M.; Faller, D.; Chakraborty, S. Debugging FPGA-accelerated real-time systems. 2020 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), Sydney NSW, Australia, 21-24 April; IEEE: New York, United States, 2020; pp. 350-363."},{"key":"e_1_3_3_1_5_2","volume":"2018","author":"Yap J.W.","unstructured":"Yap, J.W.; bin Mohd Yussof, Z.; bin Salim, S.I.; Lim, K.C. Fixed point implementation of tiny-yolo-v2 using opencl on fpga. Int J Adv Comput Sci Appl 2018, 9.","journal-title":"Int J Adv Comput Sci Appl"},{"key":"e_1_3_3_1_6_2","first-page":"779","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas Neveda, United States, 26-30 June; 2016;","author":"Redmon J.","unstructured":"Redmon, J.; Divvala, S.; Girshick, R.; Farhadi, A. You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas Neveda, United States, 26-30 June; 2016; pp. 779-788."},{"key":"e_1_3_3_1_7_2","volume-title":"Real-Time Plant Health Detection Using Deep Convolutional Neural Networks. Agriculture","author":"Khalid M.","year":"2023","unstructured":"Khalid, M.; Sarfraz, M.S.; Iqbal, U.; Aftab, M.U.; Niedba\u0142a, G.; Rauf, H.T. Real-Time Plant Health Detection Using Deep Convolutional Neural Networks. Agriculture 2023, 13, 510."},{"key":"e_1_3_3_1_8_2","first-page":"685","volume-title":"Taiwan, China, 10\u201315 January","author":"Psaltis A.","year":"2021","unstructured":"Psaltis, A.; Dimou, A.; Alvarez, F.; Daras, P. Flow R-CNN: Flow-enhanced object detection. Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, Taiwan, China, 10\u201315 January; Springer International Publishing: Berlin, German, 2021; pp. 685-700,"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2019.03595"},{"key":"e_1_3_3_1_10_2","volume-title":"Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767","author":"Redmon J.","year":"2018","unstructured":"Redmon, J.; Farhadi, A. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018 (Cornell University)."},{"key":"e_1_3_3_1_11_2","volume-title":"YOLO-based model for automatic detection of broiler pathological phenomena through visual and thermal images in intensive poultry houses. Agriculture","author":"Elmessery W.M.","year":"2023","unstructured":"Elmessery, W.M. YOLO-based model for automatic detection of broiler pathological phenomena through visual and thermal images in intensive poultry houses. Agriculture 2023, 13, 1527."},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICACCS51430.2021.9442065"},{"key":"e_1_3_3_1_13_2","first-page":"37","volume-title":"12th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'06)","author":"He T.","year":"2006","unstructured":"He, T. Achieving real-time target tracking usingwireless sensor networks. 12th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'06), San Jose California, United States, 4-7 April; IEEE: New York, United States, 2006; pp. 37-48."},{"key":"e_1_3_3_1_14_2","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu Hawaii, United States, 21-26 July; IEEE: New York, United States","author":"Chollet F.","unstructured":"Chollet, F. Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu Hawaii, United States, 21-26 July; IEEE: New York, United States, 2017; pp. 1251-1258."},{"key":"e_1_3_3_1_15_2","volume":"2021","author":"Zhao B.","unstructured":"Zhao, B.; Liu, S.; Liu, G.; Yang, Z.; Ma, Z.; Fu, H. Efficient Object Detection based on Deep Feature Fusion Network. J Phys Conf Ser 2021, 1848, 012005.","journal-title":"Deep Feature Fusion Network. J Phys Conf Ser"},{"key":"e_1_3_3_1_16_2","first-page":"2961","volume":"2018","author":"Porambage P.","unstructured":"Porambage, P.; Okwuibe, J.; Liyanage, M.; Ylianttila, M.; Taleb, T. Survey on multi-access edge computing for internet of things realization. Ieee Commun Surv Tut 2018, 20, 2961-2991.","journal-title":"Ieee Commun Surv Tut"},{"key":"e_1_3_3_1_17_2","first-page":"1697","volume":"2019","author":"Liu S.","unstructured":"Liu, S.; Liu, L.; Tang, J.; Yu, B.; Wang, Y.; Shi, W. Edge computing for autonomous driving: Opportunities and challenges. P Ieee 2019, 107, 1697-1716.","journal-title":"Ieee"},{"key":"e_1_3_3_1_18_2","first-page":"428","volume":"2019","author":"Mittal S. A","unstructured":"Mittal, S. A Survey on optimized implementation of deep learning models on the NVIDIA Jetson platform. J Syst Architect 2019, 97, 428-442.","journal-title":"J Syst Architect"},{"key":"e_1_3_3_1_19_2","first-page":"1267","volume":"2020","author":"Chen S.","unstructured":"Chen, S.; Zhan, R.; Wang, W.; Zhang, J. Learning slimming SAR ship object detector through network pruning and knowledge distillation. Ieee J-Stars 2020, 14, 1267-1282.","journal-title":"Stars"},{"key":"e_1_3_3_1_20_2","first-page":"1","volume":"2017","author":"Anwar S.","unstructured":"Anwar, S.; Hwang, K.; Sung, W. Structured pruning of deep convolutional neural networks. ACM J. Emerging Technol. Comput 2017, 13, 1-18.","journal-title":"ACM J. Emerging Technol. Comput"},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.23919\/MIPRO55190.2022.9803406"},{"key":"e_1_3_3_1_22_2","first-page":"1861","volume":"2019","author":"Nguyen D.T.","unstructured":"Nguyen, D.T.; Nguyen, T.N.; Kim, H.; Lee, H.J. A high-throughput and power-efficient FPGA implementation of YOLO CNN for object detection. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2019, 27, 1861-1873.","journal-title":"Systems"},{"key":"e_1_3_3_1_23_2","first-page":"013020","volume":"2018","author":"Yang B.","unstructured":"Yang, B.; Liu, J.; Zhou, L.; Wang, Y.; Chen, J. Quantization and training of object detection networks with low-precision weights and activations. J Electron Imaging 2018, 27, 013020-013020.","journal-title":"J Electron Imaging"},{"key":"e_1_3_3_1_24_2","volume-title":"2018 IEEE international conference on big data (big data), Seattle WA, United States, 10-13 December; IEEE: New York, United States","author":"Huang R.","unstructured":"Huang, R.; Pedoeem, J.; Chen, C. YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers. In 2018 IEEE international conference on big data (big data), Seattle WA, United States, 10-13 December; IEEE: New York, United States, 2018; pp. 2503-2510."},{"key":"e_1_3_3_1_25_2","first-page":"271","volume":"2021","author":"Wang D.","unstructured":"Wang, D.; He, D. Channel pruned YOLO V5s-based deep learning approach for rapid and accurate apple fruitlet detection before fruit thinning. Bioproc Eng 2021, 210, 271-281.","journal-title":"Bioproc Eng"},{"key":"e_1_3_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICMEW46912.2020.9105997"},{"key":"e_1_3_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2016.30"},{"key":"e_1_3_3_1_28_2","first-page":"14096","volume":"2022","author":"Zeng K.","unstructured":"Zeng, K.; Ma, Q.; Wu, J.W.; Chen, Z.; Shen, T.; Yan, C. FPGA-based accelerator for object detection: A comprehensive survey. J SUPERCOMPUT 2022, 78, 14096-14136.","journal-title":"J SUPERCOMPUT"},{"key":"e_1_3_3_1_29_2","volume":"2023","author":"Liu Y.","unstructured":"Liu, Y.; Chu, H.; Song, L.; Zhang, Z.; Wei, X.; Chen, M.; Shen, J. An improved tuna-YOLO model based on YOLO v3 for real-time tuna detection considering lightweight deployment. J Mar Sci Eng 2023, 11, 542.","journal-title":"J Mar Sci Eng"},{"key":"e_1_3_3_1_30_2","volume-title":"Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550","author":"Romero A.","year":"2014","unstructured":"Romero, A.; Ballas, N.; Kahou, S.E.; Chassang, A.; Gatta, C.; Bengio, Y. Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550, 2014 (Cornell University)."},{"key":"e_1_3_3_1_31_2","first-page":"142931","volume":"2020","author":"Li T.","unstructured":"Li, T.; Ma, Y.; Endoh, T. A systematic study of tiny YOLO3 inference: Toward compact brainware processor with less memory and logic gate. IEEE Access 2020, 8, 142931-142955.","journal-title":"IEEE Access"},{"key":"e_1_3_3_1_32_2","first-page":"1267","volume":"2020","author":"Chen S.","unstructured":"Chen, S.; Zhan, R.; Wang, W.; Zhang, J. Learning slimming SAR ship object detector through network pruning and knowledge distillation. Ieee J-Stars 2020, 14, 1267-1282.","journal-title":"Stars"},{"key":"e_1_3_3_1_33_2","first-page":"161","volume-title":"Proceedings of the 2015 ACM\/SIGDA international symposium on field-programmable gate arrays, Monterey California, United States, 22-24 February; 2015;","author":"Zhang C.","unstructured":"Zhang, C.; Li, P.; Sun, G.; Guan, Y.; Xiao, B.; Cong, J. Optimizing FPGA-based accelerator design for deep convolutional neural networks. Proceedings of the 2015 ACM\/SIGDA international symposium on field-programmable gate arrays, Monterey California, United States, 22-24 February; 2015; pp. 161-170."},{"key":"e_1_3_3_1_34_2","first-page":"1","volume-title":"Nagpur, India, 28-29 April","author":"Karapurkar S.S.","year":"2023","unstructured":"Karapurkar, S.S.; Bramhane, L.K.; Rahulkar, A.D.; Veerakumar, T. Energy Efficient Implementation of Processing Elements for CNN Hardware Accelerator. 2023 11th International Conference on Emerging Trends in Engineering & Technology-Signal and Information Processing (ICETET-SIP), Nagpur, India, 28-29 April; IEEE: New York, United States, 2023; pp. 1-5."},{"key":"e_1_3_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCST50977.2020.00092"},{"key":"e_1_3_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/CISP-BMEI56279.2022.9980321"},{"key":"e_1_3_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICIST52614.2021.9440554"},{"key":"e_1_3_3_1_38_2","first-page":"10778","volume-title":"Scalable and Efficient Object Detection,\" In 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Tan M.","year":"2020","unstructured":"Tan, M.; Pang, R.; Quoc, V. Le. \"EfficientDet: Scalable and Efficient Object Detection,\" In 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, United States, 13-19 June; IEEE: New York, United States, 2020; pp. 10778-10787."}],"event":{"name":"IoTML 2024: 2024 4th International Conference on Internet of Things and Machine Learning","location":"Nanchang China","acronym":"IoTML 2024"},"container-title":["Proceedings of the 2024 4th  International Conference on Internet of Things and Machine Learning"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3697467.3697591","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3697467.3697591","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T21:17:30Z","timestamp":1750281450000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3697467.3697591"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,9]]},"references-count":37,"alternative-id":["10.1145\/3697467.3697591","10.1145\/3697467"],"URL":"https:\/\/doi.org\/10.1145\/3697467.3697591","relation":{},"subject":[],"published":{"date-parts":[[2024,8,9]]},"assertion":[{"value":"2024-11-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}