{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T07:10:21Z","timestamp":1769584221161,"version":"3.49.0"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T00:00:00Z","timestamp":1721779200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T00:00:00Z","timestamp":1721779200000},"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":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s11432-023-3958-4","type":"journal-article","created":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T19:03:22Z","timestamp":1722020602000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A comprehensive analysis of DAC-SDC FPGA low power object detection challenge"],"prefix":"10.1007","volume":"67","author":[{"given":"Jingwei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoqing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinye","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,24]]},"reference":[{"key":"3958_CR1","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.neucom.2018.09.038","volume":"323","author":"Q Zhang","year":"2019","unstructured":"Zhang Q, Zhang M, Chen T, et al. Recent advances in convolutional neural network acceleration. Neurocomputing, 2019, 323: 37\u201351","journal-title":"Neurocomputing"},{"key":"3958_CR2","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.ins.2021.12.020","volume":"587","author":"G Li","year":"2022","unstructured":"Li G, Zhang M, Wang J, et al. SCWC: structured channel weight sharing to compress convolutional neural networks. Inf Sci, 2022, 587: 82\u201396","journal-title":"Inf Sci"},{"key":"3958_CR3","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: Proceedings of International Conference on Learning Representations, 2015"},{"key":"3958_CR4","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"3958_CR5","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, et al. Identity mappings in deep residual networks. In: Proceedings of European Conference on Computer Vision, 2016. 630\u2013645","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"3958_CR6","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, van der Maaten L, et al. Densely connected convolutional networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2017. 2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"key":"3958_CR7","doi-asserted-by":"publisher","first-page":"107610","DOI":"10.1016\/j.patcog.2020.107610","volume":"109","author":"G Li","year":"2021","unstructured":"Li G, Zhang M, Li J, et al. Efficient densely connected convolutional neural networks. Pattern Recogn, 2021, 109: 107610","journal-title":"Pattern Recogn"},{"key":"3958_CR8","doi-asserted-by":"publisher","first-page":"149403","DOI":"10.1007\/s11432-021-3242-6","volume":"65","author":"B Liu","year":"2022","unstructured":"Liu B, Zhang Z, Cai H, et al. Self-compensation tensor multiplication unit for adaptive approximate computing in low-power CNN processing. Sci China Inf Sci, 2022, 65: 149403","journal-title":"Sci China Inf Sci"},{"key":"3958_CR9","doi-asserted-by":"publisher","first-page":"200403","DOI":"10.1007\/s11432-023-3800-9","volume":"66","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Chen J, Chen X, et al. From macro to microarchitecture: reviews and trends of SRAM-based compute-in-memory circuits. Sci China Inf Sci, 2023, 66: 200403","journal-title":"Sci China Inf Sci"},{"key":"3958_CR10","doi-asserted-by":"publisher","first-page":"102898","DOI":"10.1016\/j.dsp.2020.102898","volume":"108","author":"G Li","year":"2021","unstructured":"Li G, Shen X, Li J, et al. Diagonal-kernel convolutional neural networks for image classification. Digital Signal Process, 2021, 108: 102898","journal-title":"Digital Signal Process"},{"key":"3958_CR11","doi-asserted-by":"publisher","first-page":"12860","DOI":"10.1007\/s10489-021-03152-1","volume":"52","author":"G Li","year":"2022","unstructured":"Li G, Zhang M, Zhang Y, et al. Efficient channel expansion and pyramid depthwise-pointwise-depthwise neural networks. Appl Intell, 2022, 52: 12860\u201312872","journal-title":"Appl Intell"},{"key":"3958_CR12","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/s11554-021-01161-4","volume":"19","author":"G Li","year":"2022","unstructured":"Li G, Zhang M, Zhang Q, et al. Efficient binary 3D convolutional neural network and hardware accelerator. J Real-Time Image Proc, 2022, 19: 61\u201371","journal-title":"J Real-Time Image Proc"},{"key":"3958_CR13","doi-asserted-by":"publisher","first-page":"109571","DOI":"10.1016\/j.knosys.2022.109571","volume":"253","author":"G Li","year":"2022","unstructured":"Li G, Zhang M, Zhang J, et al. OGCNet: overlapped group convolution for deep convolutional neural networks. Knowledge-Based Syst, 2022, 253: 109571","journal-title":"Knowledge-Based Syst"},{"key":"3958_CR14","doi-asserted-by":"publisher","first-page":"129401","DOI":"10.1007\/s11432-021-3407-2","volume":"66","author":"W Shan","year":"2023","unstructured":"Shan W, Cui Y, Dai W, et al. An efficient path delay variability model for wide-voltage-range digital circuits. Sci China Inf Sci, 2023, 66: 129401","journal-title":"Sci China Inf Sci"},{"key":"3958_CR15","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1109\/TPAMI.2019.2932429","volume":"43","author":"X Xu","year":"2021","unstructured":"Xu X, Zhang X, Yu B, et al. DAC-SDC low power object detection challenge for UAV applications. IEEE Trans Pattern Anal Mach Intell, 2021, 43: 392\u2013403","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3958_CR16","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.1038\/s42256-022-00567-4","volume":"4","author":"Z Jia","year":"2022","unstructured":"Jia Z, Xu X, Hu J, et al. Low-power object-detection challenge on unmanned aerial vehicles. Nat Mach Intell, 2022, 4: 1265\u20131266","journal-title":"Nat Mach Intell"},{"key":"3958_CR17","doi-asserted-by":"crossref","unstructured":"Li G, Zhang J, Zhang M, et al. An efficient FPGA implementation for real-time and low-power UAV object detection. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), 2022. 1387\u20131391","DOI":"10.1109\/ISCAS48785.2022.9937449"},{"key":"3958_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2022.02.071","volume":"490","author":"G Li","year":"2022","unstructured":"Li G, Zhang J, Zhang M, et al. Efficient depthwise separable convolution accelerator for classification and UAV object detection. Neurocomputing, 2022, 490: 1\u201316","journal-title":"Neurocomputing"},{"key":"3958_CR19","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.compag.2015.03.019","volume":"114","author":"J Torres-S\u00e1nchez","year":"2015","unstructured":"Torres-S\u00e1nchez J, L\u00f3pez-Granados F, Pe\u00f1a J M. An automatic object-based method for optimal thresholding in UAV images: application for vegetation detection in herbaceous crops. Comput Electron Agr, 2015, 114: 43\u201352","journal-title":"Comput Electron Agr"},{"key":"3958_CR20","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1109\/MGRS.2021.3115137","volume":"10","author":"X Wu","year":"2022","unstructured":"Wu X, Li W, Hong D, et al. Deep learning for unmanned aerial vehicle-based object detection and tracking: a survey. IEEE Geosci Remote Sens Mag, 2022, 10: 91\u2013124","journal-title":"IEEE Geosci Remote Sens Mag"},{"key":"3958_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, Deng J, Su H, et al. ImageNet large scale visual recognition challenge. Int J Comput Vis, 2015, 115: 211\u2013252","journal-title":"Int J Comput Vis"},{"key":"3958_CR22","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams C K I, et al. The pascal visual object classes (VOC) challenge. Int J Comput Vis, 2010, 88: 303\u2013338","journal-title":"Int J Comput Vis"},{"key":"3958_CR23","unstructured":"Zeng S, Chen W, Huang T, et al. DAC2018-TGIIT. 2018. https:\/\/github.com\/hirayaku\/DAC2018-TGIIF"},{"key":"3958_CR24","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, et al. SSD: single shot multibox detector. In: Proceedings of European Conference on Computer Vision, 2016. 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"3958_CR25","unstructured":"Zeng S, Kara K, Zhang C, et al. DAC2018-systemsETHZ. 2018. https:\/\/github.com\/fpgasystems\/spooNN"},{"key":"3958_CR26","unstructured":"Iandola F N, Moskewicz M W, Ashraf K, et al. Squeezenet: alexnet-level accuracy with 50x fewer parameters and < 0.5 MB model size. 2016. ArXiv:1602.07360"},{"key":"3958_CR27","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S K, Girshick R B, et al. You only look once: unified, real-time object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016. 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"3958_CR28","doi-asserted-by":"publisher","first-page":"107281","DOI":"10.1016\/j.patcog.2020.107281","volume":"105","author":"H Qin","year":"2020","unstructured":"Qin H, Gong R, Liu X, et al. Binary neural networks: a survey. Pattern Recogn, 2020, 105: 107281","journal-title":"Pattern Recogn"},{"key":"3958_CR29","unstructured":"Hao C, Li Y, Huang S, et al. DAC2018-iSmartDNN. 2018. https:\/\/github.com\/onioncc\/iSmartDNN"},{"key":"3958_CR30","unstructured":"Howard A G, Zhu M, Chen B, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications. 2017. ArXiv:1704.04861"},{"key":"3958_CR31","unstructured":"Zhang X, Lu H, Hao C, et al. Skynet: a hardware-efficient method for object detection and tracking on embedded systems. In: Proceedings of Machine Learning and Systems, 2020"},{"key":"3958_CR32","unstructured":"Zhang X, Hao C, Li Y, et al. A Bi-directional Co-design approach to enable deep learning on IoT devices. 2019. ArXiv:1905.08369"},{"key":"3958_CR33","doi-asserted-by":"crossref","unstructured":"Hao C, Zhang X, Li Y, et al. FPGA\/DNN Co-design: an efficient design methodology for IoT intelligence on the edge. In: Proceedings of the 56th ACM\/IEEE Design Automation Conference (DAC), 2019. 1\u20136","DOI":"10.1145\/3316781.3317829"},{"key":"3958_CR34","unstructured":"Zhang X, Hao C, Lu H, et al. SkyNet: a champion model for dac-sdc on low power object detection. 2019. ArXiv:1906.10327"},{"key":"3958_CR35","doi-asserted-by":"publisher","first-page":"1691","DOI":"10.1109\/TCAD.2022.3207320","volume":"42","author":"B Zhao","year":"2023","unstructured":"Zhao B, Xia T, Zhai H, et al. REMAP: a spatiotemporal CNN accelerator optimization methodology and toolkit thereof. IEEE Trans Comput-Aided Des Integr Circ Syst, 2023, 42: 1691\u20131704","journal-title":"IEEE Trans Comput-Aided Des Integr Circ Syst"},{"key":"3958_CR36","unstructured":"Zhao C, Zhao W, Xia T, et al. DAC2019-XJTU-Tripler. 2019. https:\/\/github.com\/xjtuiair-cag\/XJTU-Tripler"},{"key":"3958_CR37","doi-asserted-by":"crossref","unstructured":"Bao Z, Guo J, Li X, et al. MSCU: accelerating CNN inference with multiple sizes of compute unit on FPGAs. In: Proceedings of IEEE International Symposium on Embedded Multicore\/Many-core Systems-on-Chip, 2021. 106\u2013113","DOI":"10.1109\/MCSoC51149.2021.00023"},{"key":"3958_CR38","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1109\/MM.2021.3134968","volume":"42","author":"Z Bao","year":"2022","unstructured":"Bao Z, Fu G, Zhang W, et al. LSFQ: a low-bit full integer quantization for high-performance FPGA-based CNN acceleration. IEEE Micro, 2022, 42: 8\u201315","journal-title":"IEEE Micro"},{"key":"3958_CR39","doi-asserted-by":"crossref","unstructured":"Bao Z, Zhan K, Zhang W, et al. LSFQ: a low precision full integer quantization for high-performance fpga-based CNN acceleration. In: Proceedings of IEEE Symposium in Low-Power and High-Speed Chips, 2021. 1\u20136","DOI":"10.1109\/COOLCHIPS52128.2021.9410327"},{"key":"3958_CR40","doi-asserted-by":"crossref","unstructured":"Jiang W, Yu H, Liu X, et al. TAIT: one-shot full-integer lightweight DNN quantization via tunable activation imbalance transfer. In: Proceedings of the 58th ACM\/IEEE Design Automation Conference (DAC), 2021. 1027\u20131032","DOI":"10.1109\/DAC18074.2021.9586109"},{"key":"3958_CR41","doi-asserted-by":"crossref","unstructured":"Chen S, Zhou Z, Ha Y. An ultra energy efficient streaming-based FPGA accelerator for lightweight neural network. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), 2022. 3111\u20133114","DOI":"10.1109\/ISCAS48785.2022.9937510"},{"key":"3958_CR42","doi-asserted-by":"crossref","unstructured":"Liu X, Chen Y, Ganesh P, et al. HiKonv: high throughput quantized convolution with novel bit-wise management and computation. In: Proceedings of the 27th Asia and South Pacific Design Automation Conference (ASP-DAC), 2022. 140\u2013146","DOI":"10.1109\/ASP-DAC52403.2022.9712553"},{"key":"3958_CR43","unstructured":"Du P, Deng G, Kong Y, et al. Dac2021-sjtu_microe. https:\/\/github.com\/heymesut\/SJTU_microe"},{"key":"3958_CR44","doi-asserted-by":"publisher","first-page":"1953","DOI":"10.1109\/TVLSI.2020.3002779","volume":"28","author":"C Zhu","year":"2020","unstructured":"Zhu C, Huang K, Yang S, et al. An efficient hardware accelerator for structured sparse convolutional neural networks on FPGAs. IEEE Trans VLSI Syst, 2020, 28: 1953\u20131965","journal-title":"IEEE Trans VLSI Syst"},{"key":"3958_CR45","doi-asserted-by":"publisher","first-page":"4867","DOI":"10.1109\/TCAD.2020.2968023","volume":"39","author":"D Wang","year":"2020","unstructured":"Wang D, Xu K, Guo J, et al. DSP-efficient hardware acceleration of convolutional neural network inference on FPGAs. IEEE Trans Comput-Aided Des Integr Circ Syst, 2020, 39: 4867\u20134880","journal-title":"IEEE Trans Comput-Aided Des Integr Circ Syst"},{"key":"3958_CR46","doi-asserted-by":"publisher","first-page":"122405","DOI":"10.1007\/s11432-022-3807-9","volume":"67","author":"W Wu","year":"2024","unstructured":"Wu W, Tu F, Li X, et al. SWG: an architecture for sparse weight gradient computation. Sci China Inf Sci, 2024, 67: 122405","journal-title":"Sci China Inf Sci"},{"key":"3958_CR47","unstructured":"Zhang J, Cao X, Zhang Y, et al. Dac-sdc-2022-seuer. https:\/\/github.com\/AiArtisan\/dac_sdc_2022_champion"},{"key":"3958_CR48","doi-asserted-by":"crossref","unstructured":"Zhang J, Zhang M, Cao X, et al. Uint-packing: multiply your dnn accelerator performance via unsigned integer DSP packing. In: Proceedings of the 60th ACM\/IEEE Design Automation Conference (DAC), 2023. 1\u20136","DOI":"10.1109\/DAC56929.2023.10247773"},{"key":"3958_CR49","unstructured":"Steiner G, Philofsky B. Managing power and performance with the Zynq UltraScale+ MPSoC. In: Proceedings of White Paper: Zynq UltraScale+ MPSoC, WP482 (v1.1), 2016"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-023-3958-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-023-3958-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-023-3958-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:40:55Z","timestamp":1758310855000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-023-3958-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,24]]},"references-count":49,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["3958"],"URL":"https:\/\/doi.org\/10.1007\/s11432-023-3958-4","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,24]]},"assertion":[{"value":"20 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"182401"}}