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proposes an efficient dynamic reconfigurable CNN accelerator (EDRCA) for FPGAs to tackle the issues of limited hardware resources and low energy efficiency in the deployment of convolutional neural networks on embedded edge computing devices. First, a configuration layer sequence optimization method is proposed to minimize the configuration time overhead and improve performance. Second, accelerator templates for dynamic regions are designed to create a unified high-speed interface and enhance operational performance. The dynamic reconfigurable technology is applied on the Xilinx KV260 FPGA platform to design the EDRCA accelerator, resolving the hardware resource constraints in traditional accelerator design. The YOLOV2-TINY object detection network is used to test the EDRCA accelerator on the Xilinx KV260 platform using floating point data. Results at 250 MHz show a computing performance of 75.1929 GOPS, peak power consumption of 5.25 W, and power efficiency of 13.6219 GOPS\/W, indicating the potential of the EDRCA accelerator for edge intelligence computing.<\/jats:p>","DOI":"10.3390\/info14030194","type":"journal-article","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T03:09:37Z","timestamp":1679281777000},"page":"194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Efficient Dynamic Reconfigurable CNN Accelerator for Edge Intelligence Computing on FPGA"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3726-032X","authenticated-orcid":false,"given":"Kaisheng","family":"Shi","sequence":"first","affiliation":[{"name":"College of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi\u2019an 710021, China"}]},{"given":"Mingwei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi\u2019an 710021, China"}]},{"given":"Xin","family":"Tan","sequence":"additional","affiliation":[{"name":"College of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi\u2019an 710021, China"}]},{"given":"Qianghua","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi\u2019an 710021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2104-9298","authenticated-orcid":false,"given":"Tao","family":"Lei","sequence":"additional","affiliation":[{"name":"College of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi\u2019an 710021, China"},{"name":"Shaanxi Joint Laboratory of Artificial Intelligence, Shaanxi University of Science and Technology, Xi\u2019an 710021, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.1109\/TED.2023.3236331","article-title":"Hybrid Multilevel STT\/DSHE Memory for Efficient CNN Training","volume":"70","author":"Nisar","year":"2023","journal-title":"IEEE Trans. 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