{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T18:26:50Z","timestamp":1775845610308,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T00:00:00Z","timestamp":1668038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2018R1D1A1B09043286"],"award-info":[{"award-number":["2018R1D1A1B09043286"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the recent growth of the Internet of Things (IoT) and the demand for faster computation, quantized neural networks (QNNs) or QNN-enabled IoT can offer better performance than conventional convolution neural networks (CNNs). With the aim of reducing memory access costs and increasing the computation efficiency, QNN-enabled devices are expected to transform numerous industrial applications with lower processing latency and power consumption. Another form of QNN is the binarized neural network (BNN), which has 2 bits of quantized levels. In this paper, CNN-, QNN-, and BNN-based pattern recognition techniques are implemented and analyzed on an FPGA. The FPGA hardware acts as an IoT device due to connectivity with the cloud, and QNN and BNN are considered to offer better performance in terms of low power and low resource use on hardware platforms. The CNN and QNN implementation and their comparative analysis are analyzed based on their accuracy, weight bit error, RoC curve, and execution speed. The paper also discusses various approaches that can be deployed for optimizing various CNN and QNN models with additionally available tools. The work is performed on the Xilinx Zynq 7020 series Pynq Z2 board, which serves as our FPGA-based low-power IoT device. The MNIST and CIFAR-10 databases are considered for simulation and experimentation. The work shows that the accuracy is 95.5% and 79.22% for the MNIST and CIFAR-10 databases, respectively, for full precision (32-bit), and the execution time is 5.8 ms and 18 ms for the MNIST and CIFAR-10 databases, respectively, for full precision (32-bit).<\/jats:p>","DOI":"10.3390\/s22228694","type":"journal-article","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T21:22:02Z","timestamp":1668115322000},"page":"8694","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Pattern Classification Using Quantized Neural Networks for FPGA-Based Low-Power IoT Devices"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4263-9083","authenticated-orcid":false,"given":"Manas Ranjan","family":"Biswal","sequence":"first","affiliation":[{"name":"Department of Intelligent Robot Engineering, Pukyong National University, Busan 48513, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4646-763X","authenticated-orcid":false,"given":"Tahesin Samira","family":"Delwar","sequence":"additional","affiliation":[{"name":"Department of Intelligent Robot Engineering, Pukyong National University, Busan 48513, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7447-7253","authenticated-orcid":false,"given":"Abrar","family":"Siddique","sequence":"additional","affiliation":[{"name":"Department of Intelligent Robot Engineering, Pukyong National University, Busan 48513, Republic of Korea"},{"name":"Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1086-3822","authenticated-orcid":false,"given":"Prangyadarsini","family":"Behera","sequence":"additional","affiliation":[{"name":"Department of Intelligent Robot Engineering, Pukyong National University, Busan 48513, Republic of Korea"}]},{"given":"Yeji","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Intelligent Robot Engineering, Pukyong National University, Busan 48513, Republic of Korea"}]},{"given":"Jee-Youl","family":"Ryu","sequence":"additional","affiliation":[{"name":"Department of Intelligent Robot Engineering, Pukyong National University, Busan 48513, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"114064","DOI":"10.1016\/j.eswa.2020.114064","article-title":"Improved salient object detection using hybrid Convolution Recurrent Neural Network","volume":"166","author":"Kousik","year":"2021","journal-title":"Expert Syst. 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