{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T16:13:48Z","timestamp":1780676028969,"version":"3.54.1"},"reference-count":33,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,4,15]],"date-time":"2022-04-15T00:00:00Z","timestamp":1649980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>A plethora of image and video-related applications involve complex processes that impose the need for hardware accelerators to achieve real-time performance. Among these, notable applications include the Machine Learning (ML) tasks using Convolutional Neural Networks (CNNs) that detect objects in image frames. Aiming at contributing to the CNN accelerator solutions, the current paper focuses on the design of Field-Programmable Gate Arrays (FPGAs) for CNNs of limited feature space to improve performance, power consumption and resource utilization. The proposed design approach targets the designs that can utilize the logic and memory resources of a single FPGA device and benefit mainly the edge, mobile and on-board satellite (OBC) computing; especially their image-processing- related applications. This work exploits the proposed approach to develop an FPGA accelerator for vessel detection on a Xilinx Virtex 7 XC7VX485T FPGA device (Advanced Micro Devices, Inc, Santa Clara, CA, USA). The resulting architecture operates on RGB images of size 80\u00d780 or sliding windows; it is trained for the \u201cShips in Satellite Imagery\u201d and by achieving frequency 270 MHz, completing the inference in 0.687 ms and consuming 5 watts, it validates the approach.<\/jats:p>","DOI":"10.3390\/jimaging8040114","type":"journal-article","created":{"date-parts":[[2022,4,18]],"date-time":"2022-04-18T04:21:28Z","timestamp":1650255688000},"page":"114","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Resources and Power Efficient FPGA Accelerators for Real-Time Image Classification"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5090-0582","authenticated-orcid":false,"given":"Angelos","family":"Kyriakos","sequence":"first","affiliation":[{"name":"Electronics Laboratory, Faculty of Physics, National and Kapodistrian University of Athens, 15772 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4286-0098","authenticated-orcid":false,"given":"Elissaios-Alexios","family":"Papatheofanous","sequence":"additional","affiliation":[{"name":"Electronics Laboratory, Faculty of Physics, National and Kapodistrian University of Athens, 15772 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charalampos","family":"Bezaitis","sequence":"additional","affiliation":[{"name":"Electronics Laboratory, Faculty of Physics, National and Kapodistrian University of Athens, 15772 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dionysios","family":"Reisis","sequence":"additional","affiliation":[{"name":"Electronics Laboratory, Faculty of Physics, National and Kapodistrian University of Athens, 15772 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,15]]},"reference":[{"key":"ref_1","unstructured":"Mordvintsev, A., Olah, C., and Tyka, M. (2022, April 13). Inceptionism: Going Deeper into Neural Networks. 2015. Available online: https:\/\/research.googleblog.com\/2015\/06\/inceptionism-going-deeper-into-neural.html."},{"key":"ref_2","unstructured":"Abdelouahab, K., Pelcat, M., S\u00e9rot, J., and Berry, F. (2018). Accelerating CNN inference on FPGAs: A Survey. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s42452-019-1903-4","article-title":"Shallow convolutional neural network for image classification","volume":"2","author":"Lei","year":"2020","journal-title":"SN Appl. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kyriakos, A., Kitsakis, V., Louropoulos, A., Papatheofanous, E.A., and Patronas, G. (2019, January 1\u20133). High Performance Accelerator for CNN Applications. Proceedings of the 2019 29th International Symposium on Power and Timing Modeling, Optimization and Simulation, Rhodes, Greece.","DOI":"10.1109\/PATMOS.2019.8862166"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, H., Lin, Z., Shen, X., and Brandt, J. (2015, January 7\u201312). A convolutional neural network cascade for face detection. Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299170"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sermanet, P., and LeCun, Y. (August, January 31). Traffic sign recognition with multi-scale Convolutional Networks. Proceedings of the 2011 International Joint Conference on Neural Networks, San Jose, CA, USA.","DOI":"10.1109\/IJCNN.2011.6033589"},{"key":"ref_7","unstructured":"(2022, April 13). Airbus Ship Detection Challenge. Available online: https:\/\/www.kaggle.com\/c\/airbus-ship-detection."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gorokhovatskyi, O., and Peredrii, O. (2018, January 21\u201325). Shallow Convolutional Neural Networks for Pattern Recognition Problems. Proceedings of the 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine.","DOI":"10.1109\/DSMP.2018.8478540"},{"key":"ref_9","unstructured":"(2022, April 13). Planet: Ships-in-Satellite-Imagery. Available online: https:\/\/www.kaggle.com\/rhammell\/ships-in-satellite-imagery."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/MM.2015.10","article-title":"Always-on Vision Processing Unit for Mobile Applications","volume":"35","author":"Barry","year":"2015","journal-title":"IEEE Micro"},{"key":"ref_11","unstructured":"Espa\u00f1a Navarro, J., Samuelsson, A., Gingsj\u00f6, H., Barendt, J., Dunne, A., Buckley, L., Reisis, D., Kyriakos, A., Papatheofanous, E.A., and Bezaitis, C. (2021, January 14\u201317). High-Performance Compute Board\u2014A Fault-Tolerant Module for On-Boards Vision Processing. Proceedings of the 2nd European Workshop on On-Board Data Processing (OBDP 2021), Online."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rapuano, E., Meoni, G., Pacini, T., Dinelli, G., Furano, G., Giuffrida, G., and Fanucci, L. (2021). An FPGA-Based Hardware Accelerator for CNNs Inference on Board Satellites: Benchmarking with Myriad 2-Based Solution for the CloudScout Case Study. Remote Sens., 13.","DOI":"10.3390\/rs13081518"},{"key":"ref_13","unstructured":"(2022, April 13). Nvidia Jetson Nano. Available online: https:\/\/developer.nvidia.com\/embedded\/jetson-nano-developer-kit."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kim, J.H., Grady, B., Lian, R., Brothers, J., and Anderson, J.H. (2017, January 5\u20138). FPGA-based CNN inference accelerator synthesized from multi-threaded C software. Proceedings of the 2017 30th IEEE International System-on-Chip Conference (SOCC), Munich, Germany.","DOI":"10.1109\/SOCC.2017.8226056"},{"key":"ref_15","unstructured":"Solovyev, R.A., Kalinin, A.A., Kustov, A.G., Telpukhov, D.V., and Ruhlov, V.S. (2018). FPGA Implementation of Convolutional Neural Networks with Fixed-Point Calculations. arXiv, Available online: https:\/\/arxiv.org\/abs\/1808.09945v1."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, C., Li, P., Sun, G., Guan, Y., Xiao, B., and Cong, J. (2015, January 22\u201324). Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks. Proceedings of the 2015 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, Monterey, CA, USA.","DOI":"10.1145\/2684746.2689060"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sankaradas, M., Jakkula, V., Cadambi, S., Chakradhar, S., Durdanovic, I., Cosatto, E., and Graf, H.P. (2009, January 7\u20139). A Massively Parallel Coprocessor for Convolutional Neural Networks. Proceedings of the 2009 20th IEEE International Conference on Application-specific Systems, Architectures and Processors, Boston, MA, USA.","DOI":"10.1109\/ASAP.2009.25"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Peemen, M., Setio, A.A.A., Mesman, B., and Corporaal, H. (2013, January 6\u20139). Memory-centric accelerator design for Convolutional Neural Networks. Proceedings of the 2013 IEEE 31st International Conference on Computer Design (ICCD), Asheville, NC, USA.","DOI":"10.1109\/ICCD.2013.6657019"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, B., Zou, D., Feng, L., Feng, S., Fu, P., and Li, J. (2019). An FPGA-Based CNN Accelerator Integrating Depthwise Separable Convolution. Electronics, 8.","DOI":"10.3390\/electronics8030281"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Pelcat, M., Bourrasset, C., Maggiani, L., and Berry, F. (2016, January 17\u201321). Design productivity of a high level synthesis compiler versus HDL. Proceedings of the 2016 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS), Agios Konstantinos, Greece.","DOI":"10.1109\/SAMOS.2016.7818341"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Gao, X., Guo, X., Liu, J., Wang, E., Mullins, R., Cheung, P.Y.K., Constantinides, G., and Xu, C.Z. (2019, January 9\u201313). Automatic Generation of Multi-Precision Multi-Arithmetic CNN Accelerators for FPGAs. Proceedings of the 2019 International Conference on Field-Programmable Technology (ICFPT), Tianjin, China.","DOI":"10.1109\/ICFPT47387.2019.00014"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2295","DOI":"10.1109\/JPROC.2017.2761740","article-title":"Efficient Processing of Deep Neural Networks: A Tutorial and Survey","volume":"105","author":"Sze","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lamoureux, J., and Luk, W. (2008, January 22\u201325). An Overview of Low-Power Techniques for Field-Programmable Gate Arrays. Proceedings of the 2008 NASA\/ESA Conference on Adaptive Hardware and Systems, Noordwijk, The Netherlands.","DOI":"10.1109\/AHS.2008.71"},{"key":"ref_24","unstructured":"Dekker, R., Bouma, H., den Breejen, E., van den Broek, B., Hanckmann, P., Hogervorst, M., Mohamoud, A., Schoemaker, R., Sijs, J., and Tan, R. (2013, January 4\u20136). Maritime situation awareness capabilities from satellite and terrestrial sensor systems. Proceedings of the MAST (Maritime Systems and Technology) Europe Conference 2013, Gdansk, Poland."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.12.033","article-title":"Vessel detection and classification from spaceborne optical images: A literature survey","volume":"207","author":"Kanjir","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_26","unstructured":"Girshick, R.B., Donahue, J., Darrell, T., and Malik, J. (2013). Rich feature hierarchies for accurate object detection and semantic segmentation. CoRR, abs\/1311.2524. Available online: https:\/\/arxiv.org\/abs\/1311.2524."},{"key":"ref_27","unstructured":"Ren, S., He, K., Girshick, R.B., and Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. CoRR, abs\/1506.01497. Available online: https:\/\/arxiv.org\/abs\/1506.01497."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S.K., Girshick, R.B., and Farhadi, A. (2015). You Only Look Once: Unified, Real-Time Object Detection. CoRR, abs\/1506.02640. Available online: https:\/\/arxiv.org\/abs\/1506.02640.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C., and Berg, A.C. (2015). SSD: Single Shot MultiBox Detector. CoRR, abs\/1512.02325. Available online: https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zhang, W., Sun, H., and Xue, B. (2019). Embedded Deep Learning for Ship Detection and Recognition. Future Internet, 11.","DOI":"10.3390\/fi11020053"},{"key":"ref_31","unstructured":"Yu, J.-Y., Huang, D., Wang, L.-Y., Guo, J., and Wang, Y.-H. (2016, January 6\u201310). A real-time on-board ship targets detection method for optical remote sensing satellite. Proceedings of the 2016 IEEE 13th International Conference on Signal Processing (ICSP), Chengdu, China."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5517414","DOI":"10.1109\/TGRS.2021.3125567","article-title":"The -Sat-1 Mission: The First On-Board Deep Neural Network Demonstrator for Satellite Earth Observation","volume":"60","author":"Giuffrida","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MAES.2020.3008468","article-title":"Towards the Use of Artificial Intelligence on the Edge in Space Systems: Challenges and Opportunities","volume":"35","author":"Furano","year":"2020","journal-title":"IEEE Aerosp. Electron. Syst. 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