{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T13:49:59Z","timestamp":1787320199702,"version":"build-2736575974"},"reference-count":33,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,5,30]],"date-time":"2020-05-30T00:00:00Z","timestamp":1590796800000},"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":["2017R1E1A1A03070102"],"award-info":[{"award-number":["2017R1E1A1A03070102"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["IITP-2020-2018-0-01421"],"award-info":[{"award-number":["IITP-2020-2018-0-01421"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents an always-on Complementary Metal Oxide Semiconductor (CMOS) image sensor (CIS) using an analog convolutional neural network for image classification in mobile applications. To reduce the power consumption as well as the overall processing time, we propose analog convolution circuits for computing convolution, max-pooling, and correlated double sampling operations without operational transconductance amplifiers. In addition, we used the voltage-mode MAX circuit for max pooling in the analog domain. After the analog convolution processing, the image data were reduced by 99.58% and were converted to digital with a 4-bit single-slope analog-to-digital converter. After the conversion, images were classified by the fully connected processor, which is traditionally performed in the digital domain. The measurement results show that we achieved an 89.33% image classification accuracy. The prototype CIS was fabricated in a 0.11 \u03bcm 1-poly 4-metal CIS process with a standard 4T-active pixel sensor. The image resolution was 160 \u00d7 120, and the total power consumption of the proposed CIS was 1.12 mW with a 3.3 V supply voltage and a maximum frame rate of 120.<\/jats:p>","DOI":"10.3390\/s20113101","type":"journal-article","created":{"date-parts":[[2020,6,2]],"date-time":"2020-06-02T09:19:27Z","timestamp":1591089567000},"page":"3101","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Design of an Always-On Image Sensor Using an Analog Lightweight Convolutional Neural Network"],"prefix":"10.3390","volume":"20","author":[{"given":"Jaihyuk","family":"Choi","sequence":"first","affiliation":[{"name":"Department of Semiconductor Science, Dongguk University-Seoul, Seoul 04620, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6142-2694","authenticated-orcid":false,"given":"Sungjae","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul 04620, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1912-5853","authenticated-orcid":false,"given":"Youngdoo","family":"Son","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul 04620, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3463-5760","authenticated-orcid":false,"given":"Soo Youn","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Semiconductor Science, Dongguk University-Seoul, Seoul 04620, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,30]]},"reference":[{"key":"ref_1","unstructured":"Evans, D. (2011). The Internet of Things\u2014How the Next Evolution of the Internet Is Changing Everything, White Paper, Cisco IBSG."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.imavis.2016.03.018","article-title":"Face recognition for authentication on mobile devices","volume":"55","author":"Fernandez","year":"2016","journal-title":"Image Vis. Comput."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Fathy, M.E., Patel, V.M., and Chellappa, R. (2015, January 19\u201324). Face-based active authentication on mobile devices. Proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, Australia.","DOI":"10.1109\/ICASSP.2015.7178258"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Park, K., Song, M., and Kim, S.Y. (2018). The design of a single-bit CMOS image sensor for iris recognition applications. Sensors, 18.","DOI":"10.3390\/s18020669"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1109\/JSSC.2015.2470526","article-title":"Always-on CMOS image sensor for mobile and wearable devices","volume":"51","author":"Choi","year":"2016","journal-title":"IEEE J. Solid State Circuits"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kim, C., Bong, K., Hong, I., Lee, K., Choi, S., and Yoo, H. (2017, January 11\u201314). An ultra-low-power and mixed-mode event-driven face detection SoC for always-on mobile applications. Proceedings of the IEEE European Solid State Circuits Conference, Leuven, Belgium.","DOI":"10.1109\/ESSCIRC.2017.8094574"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1109\/JSSC.2017.2661838","article-title":"A 23-mW face recognition processor with mostly-read 5T memory in 40-nm CMOS","volume":"52","author":"Jeon","year":"2017","journal-title":"IEEE J. Solid State Circuits"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Tsai, A., Ou, Y., Hsu, L., and Wang, J. (2018, January 23\u201326). Efficient and effective multi-person and multi-angle face recognition based on deep CNN architecture. Proceedings of the International Conference on Orange Technologies (ICOT), Bali, Indonesia.","DOI":"10.1109\/ICOT.2018.8705876"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ahamed, H., Alam, I., and Islam, M.M. (2018, January 22\u201324). HOG-CNN based real time face recognition. Proceedings of the International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE), Gazipur, Bangladesh.","DOI":"10.1109\/ICAEEE.2018.8642989"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1109\/JETCAS.2018.2839347","article-title":"Optimized hierarchical cascaded processing","volume":"8","author":"Goetschalckx","year":"2018","journal-title":"IEEE J. Emerg. Sel. Top. Circuits Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sumi, N., Baba, A., and Moshnyaga, V.G. (2014, January 20\u201322). Effect of computation offload on performance and energy consumption of mobile face recognition. Proceedings of the 2014 IEEE Workshop on Signal Processing Systems (SiPS), Belfast, UK.","DOI":"10.1109\/SiPS.2014.6986056"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1109\/JSSC.2017.2767705","article-title":"A low-power convolutional neural network face recognition processor and a CIS integrated with always-on face detector","volume":"53","author":"Bong","year":"2018","journal-title":"IEEE J. Solid State Circuits"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kim, J., Kim, C., Kim, K., and Yoo, H. (2019, January 26\u201329). An ultra-low-power analog-digital hybrid CNN face recognition processor integrated with a CIS for always-on mobile devices. Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), Sapporo, Japan.","DOI":"10.1109\/ISCAS.2019.8702698"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_15","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Stateline, NV, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Schwarz, M., Schulz, H., and Behnke, S. (2015, January 26\u201330). RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139363"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1162\/neco_a_00990","article-title":"Deep convolutional neural networks for image classification: A comprehensive review","volume":"29","author":"Rawat","year":"2017","journal-title":"Neural Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/72.554195","article-title":"Face recognition: A convolutional neural-network approach","volume":"8","author":"Lawrence","year":"1997","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Park, I., Park, C., Cheon, J., and Chae, Y. (2019, January 17\u201321). 76 mW 500 fps VGA CMOS image sensor with time-stretched single-slope ADCs achieving 1.95e\u2212 random noise. Proceedings of the 2019 IEEE International Solid-State Circuits Conference\u2014(ISSCC), San Francisco, CA, USA.","DOI":"10.1109\/ISSCC.2019.8662388"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Park, K., Jin, M., Kim, S.Y., and Song, M. (2017, January 5\u20138). Design of a high speed CMOS image sensor with a hybrid single-slope column ADC and a finite state machine. Proceedings of the 2017 International SoC Design Conference (ISOCC), Seoul, Korea.","DOI":"10.1109\/ISOCC.2017.8368786"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1049\/el.2019.2496","article-title":"Low-power column counter with a logical-shift algorithm for CMOS image sensors","volume":"56","author":"Park","year":"2020","journal-title":"Electron. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kim, D., Song, M., Choe, B., and Kim, S.Y. (2017). A multi-resolution mode CMOS image sensor with a novel two-step single-slope ADC for intelligent surveillance systems. Sensors, 17.","DOI":"10.3390\/s17071497"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1109\/TED.2012.2190936","article-title":"A 1.92-mega pixel cmos image sensor with column-parallel low-power and area efficient SA-ADCs","volume":"59","author":"Shin","year":"2012","journal-title":"IEEE Trans. Electron Devices"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nitta, Y. (2006, January 6\u20139). High-speed digital double sampling with analog CDS on column parallel ADC architecture for low-noise active pixel sensor. Proceedings of the 2006 IEEE International Solid State Circuits Conference\u2014Digest of Technical Papers, San Francisco, CA, USA.","DOI":"10.1109\/ISSCC.2006.1696261"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Soleimani, M., Khoei, A., Hadidi, K., and Nia, S.K. (2009, January 23\u201327). Design of high-speed high-precision voltage-mode MAX-MIN circuits with low area and low power consumption. Proceedings of the 2009 European Conference on Circuit Theory and Design, Antalya, Turkey.","DOI":"10.1109\/ECCTD.2009.5274998"},{"key":"ref_27","first-page":"318","article-title":"Learning internal representations by error propagation","volume":"1","author":"Rumelhart","year":"1986","journal-title":"Parallel Distrib. Process."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bengio, Y. (2012). Practical recommendations for gradient-based training of deep architectures. Neural Networks: Tricks of the Trade, Springer.","DOI":"10.1007\/978-3-642-35289-8_26"},{"key":"ref_29","unstructured":"Alvarez, S.A. (2002). An Exact Analytical Relation among Recall, Precision, and Classification Accuracy in Information Retrieval, Computer Science Department, Boston College. Technical Report."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1002\/(SICI)1097-4571(199401)45:1<12::AID-ASI2>3.0.CO;2-L","article-title":"The relationship between Recall and Precision","volume":"45","author":"Buckland","year":"1994","journal-title":"J. Am. Soc. Inf. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3762651","DOI":"10.1155\/2017\/3762651","article-title":"Defining an optimal cut-point value in ROC analysis: An alternative approach","volume":"2017","author":"Unal","year":"2017","journal-title":"Comput. Math. Methods Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5531","DOI":"10.3390\/s150305531","article-title":"An ultra-low power CMOS image sensor with on-chip energy harvesting and power management capability","volume":"15","author":"Cevik","year":"2015","journal-title":"Sensors"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"854","DOI":"10.5573\/JSTS.2017.17.6.854","article-title":"A low-power CMOS image sensor based on variable frame rate operation","volume":"17","author":"Choi","year":"2017","journal-title":"J. Semicond. Technol. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/11\/3101\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:34:07Z","timestamp":1760175247000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/11\/3101"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,30]]},"references-count":33,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["s20113101"],"URL":"https:\/\/doi.org\/10.3390\/s20113101","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,30]]}}}