{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:43:09Z","timestamp":1787017389871,"version":"build-2736575974"},"reference-count":26,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T00:00:00Z","timestamp":1683676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF","award":["1652866"],"award-info":[{"award-number":["1652866"]}]},{"DOI":"10.13039\/100000185","name":"DARPA","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Reconfigurable Technol. Syst."],"published-print":{"date-parts":[[2023,6,30]]},"abstract":"<jats:p>Convolutional neural network (CNN)-based object detection has achieved very high accuracy; e.g., single-shot multi-box detectors (SSDs) can efficiently detect and localize various objects in an input image. However, they require a high amount of computation and memory storage, which makes it difficult to perform efficient inference on resource-constrained hardware devices such as drones or unmanned aerial vehicles (UAVs). Drone\/UAV detection is an important task for applications including surveillance, defense, and multi-drone self-localization and formation control. In this article, we designed and co-optimized an algorithm and hardware for energy-efficient drone detection on resource-constrained FPGA devices. We trained an SSD object detection algorithm with a custom drone dataset. For inference, we employed low-precision quantization and adapted the width of the SSD CNN model. To improve throughput, we use dual-data rate operations for DSPs to effectively double the throughput with limited DSP counts. For different SSD algorithm models, we analyze accuracy or mean average precision (mAP) and evaluate the corresponding FPGA hardware utilization, DRAM communication, and throughput optimization. We evaluated the FPGA hardware for a custom drone dataset, Pascal VOC, and COCO2017. Our proposed design achieves a high mAP of 88.42% on the multi-drone dataset, with a high energy efficiency of 79 GOPS\/W and throughput of 158 GOPS using the Xilinx Zynq ZU3EG FPGA device on the Open Vision Computer version 3 (OVC3) platform. Our design achieves 1.1 to 8.7\u00d7 higher energy efficiency than prior works that used the same Pascal VOC dataset, using the same FPGA device, but at a low-power consumption of 2.54 W. For the COCO dataset, our MobileNet-V1 implementation achieved an mAP of 16.8, and 4.9 FPS\/W for energy-efficiency, which is \u223c 1.9\u00d7 higher than prior FPGA works or other commercial hardware platforms.<\/jats:p>","DOI":"10.1145\/3583074","type":"journal-article","created":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T18:14:48Z","timestamp":1676484888000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Algorithm-hardware Co-optimization for Energy-efficient Drone Detection on Resource-constrained FPGA"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4466-4824","authenticated-orcid":false,"given":"Han-Sok","family":"Suh","sequence":"first","affiliation":[{"name":"Arizona State University, Tempe, AZ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7703-5020","authenticated-orcid":false,"given":"Jian","family":"Meng","sequence":"additional","affiliation":[{"name":"Arizona State University, Tempe, AZ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6351-8865","authenticated-orcid":false,"given":"Ty","family":"Nguyen","sequence":"additional","affiliation":[{"name":"University of Pennsylvania, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3902-9391","authenticated-orcid":false,"given":"Vijay","family":"Kumar","sequence":"additional","affiliation":[{"name":"University of Pennsylvania, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6968-1180","authenticated-orcid":false,"given":"Yu","family":"Cao","sequence":"additional","affiliation":[{"name":"Arizona State University, Tempe, AZ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4551-7789","authenticated-orcid":false,"given":"Jae-Sun","family":"Seo","sequence":"additional","affiliation":[{"name":"Arizona State University, Tempe, AZ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,5,10]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICATE49685.2021.9465015"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSSE50014.2020.9219319"},{"key":"e_1_3_1_4_2","volume-title":"Conference on Machine Learning and Systems (MLSys\u201919)","author":"Choi Jungwook","year":"2019","unstructured":"Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, Zhuo Wang, and Pierce Chuang. 2019. Accurate and efficient 2-bit quantized neural networks. In Conference on Machine Learning and Systems (MLSys\u201919)."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2961959"},{"key":"e_1_3_1_7_2","article-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications","author":"Howard Andrew G.","year":"2017","unstructured":"Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017).","journal-title":"arXiv preprint arXiv:1704.04861"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00286"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2021.3119187"},{"key":"e_1_3_1_10_2","article-title":"Quantizing deep convolutional networks for efficient inference: A whitepaper","author":"Krishnamoorthi Raghuraman","year":"2018","unstructured":"Raghuraman Krishnamoorthi. 2018. Quantizing deep convolutional networks for efficient inference: A whitepaper. arXiv preprint arXiv:1806.08342 (2018).","journal-title":"arXiv preprint arXiv:1806.08342"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00301"},{"key":"e_1_3_1_12_2","volume-title":"International Conference on Learning Representations (ICLR\u201919)","author":"Li Yuhang","year":"2019","unstructured":"Yuhang Li, Xin Dong, and Wei Wang. 2019. Additive powers-of-two quantization: An efficient non-uniform discretization for neural networks. In International Conference on Learning Representations (ICLR\u201919)."},{"key":"e_1_3_1_13_2","article-title":"Rotated binary neural network","volume":"33","author":"Lin Mingbao","year":"2020","unstructured":"Mingbao Lin, Rongrong Ji, Zihan Xu, Baochang Zhang, Yan Wang, Yongjian Wu, Feiyue Huang, and Chia-Wen Lin. 2020. Rotated binary neural network. 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Shivakumar, Michael Watterson, Yash Mulgaonkar, Mikael Arguedas, Ke Sun, Sikang Liu, Bernd Pfrommer, Vijay Kumar, and Camillo J. Taylor. 2018. The open vision computer: An integrated sensing and compute system for mobile robots. CoRR abs\/1809.07674 (2018). arxiv:1809.07674http:\/\/arxiv.org\/abs\/1809.07674.","journal-title":"CoRR"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSSC.2020.3002140"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/FPL.2019.00030"},{"key":"e_1_3_1_24_2","article-title":"Product Guide of DPU (Deep Learning Processing Unit) IP","year":"2020","unstructured":"Xilinx. 2020. 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