{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:14:07Z","timestamp":1771373647211,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T00:00:00Z","timestamp":1611878400000},"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":["2019R1F1A1048115"],"award-info":[{"award-number":["2019R1F1A1048115"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"Ministry of Science, ICT and Future Planning","doi-asserted-by":"publisher","award":["IITP-2020-0-01821"],"award-info":[{"award-number":["IITP-2020-0-01821"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to limited resources of the Internet of Things (IoT) edge devices, deep neural network (DNN) inference requires collaboration with cloud server platforms, where DNN inference is partitioned and offloaded to high-performance servers to reduce end-to-end latency. As data-intensive intermediate feature space at the partitioned layer should be transmitted to the servers, efficient compression of the feature space is imperative for high-throughput inference. However, the feature space at deeper layers has different characteristics than natural images, limiting the compression performance by conventional preprocessing and encoding techniques. To tackle this limitation, we introduce a new method for compressing DNN intermediate feature space using a specialized autoencoder, called auto-tiler. The proposed auto-tiler is designed to include the tiling process and provide multiple input\/output dimensions to support various partitioned layers and compression ratios. The results show that auto-tiler achieves 18% to 67% higher percent point accuracy compared to the existing methods at the same bitrate while reducing the process latency by 73% to 81%. The dimension variability of an auto-tiler also reduces the storage overhead by 62% with negligible accuracy loss.<\/jats:p>","DOI":"10.3390\/s21030896","type":"journal-article","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T05:53:43Z","timestamp":1611899623000},"page":"896","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Auto-Tiler: Variable-Dimension Autoencoder with Tiling for Compressing Intermediate Feature Space of Deep Neural Networks for Internet of Things"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5790-1278","authenticated-orcid":false,"given":"Jeongsoo","family":"Park","sequence":"first","affiliation":[{"name":"Department of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1587-0677","authenticated-orcid":false,"given":"Jungrae","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Semiconductor Systems Engineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jong Hwan","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"174531","DOI":"10.1109\/ACCESS.2019.2956980","article-title":"Face occlusion recognition with deep learning in security framework for the IoT","volume":"7","author":"Mao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Majumder, A.J., and Izaguirre, J.A. (2020, January 13\u201317). A Smart IoT Security System for Smart-Home Using Motion Detection and Facial Recognition. Proceedings of the 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain.","DOI":"10.1109\/COMPSAC48688.2020.0-132"},{"key":"ref_3","unstructured":"Moorthy, R., Upadhya, V., Holla, V.V., Shetty, S.S., and Tantry, V. (2020, January 7\u20139). CNN based Smart Surveillance System: A Smart IoT Application Post Covid-19 Era. Proceedings of the 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Isyanto, H., Arifin, A.S., and Suryanegara, M. (2020, January 20). Design and Implementation of IoT-Based Smart Home Voice Commands for disabled people using Google Assistant. Proceedings of the 2020 International Conference on Smart Technology and Applications (ICoSTA), Surabaya, Indonesia.","DOI":"10.1109\/ICoSTA48221.2020.1570613925"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Vashistha, P., Singh, J.P., Jain, P., and Kumar, J. (2019, January 12\u201314). Raspberry Pi based voice-operated personal assistant (Neobot). Proceedings of the 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India.","DOI":"10.1109\/ICECA.2019.8821892"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chayapathy, V., Anitha, G.S., and Sharath, B. (2017, January 17\u201319). IOT based home automation by using personal assistant. Proceedings of the 2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon), Bengaluru, India.","DOI":"10.1109\/SmartTechCon.2017.8358401"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"55091","DOI":"10.1109\/ACCESS.2020.2978531","article-title":"Wireless AI in smart car: How smart a car can be?","volume":"8","author":"Xu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kodre, A., Tikone, K., Sonawane, M., Jare, P., and Shinde, P. (2018, January 30\u201331). Smart and Efficient Personal Car Assistant System. Proceedings of the 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India.","DOI":"10.1109\/I-SMAC.2018.8653752"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1145\/3093337.3037698","article-title":"Neurosurgeon: Collaborative intelligence between the cloud and mobile edge","volume":"45","author":"Kang","year":"2017","journal-title":"SIGARCH Comput. Archit. News"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Eshratifar, A.E., Esmaili, A., and Pedram, M. (2019, January 29\u201331). BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services. Proceedings of the 2019 IEEE\/ACM International Symposium on Low Power Electronics and Design (ISLPED), Lausanne, Switzerland.","DOI":"10.1109\/ISLPED.2019.8824955"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, E., Zhou, Z., and Chen, X. (2018). Edge Intelligence: On-Demand deep learning model co-inference with device-edge synergy. arXiv.","DOI":"10.1145\/3229556.3229562"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hu, C., Bao, W., Wang, D., and Liu, F. (May, January 29). Dynamic Adaptive DNN Surgery for Inference Acceleration on the Edge. Proceedings of the IEEE INFOCOM 2019\u2014IEEE Conference on Computer Communications, Paris, France.","DOI":"10.1109\/INFOCOM.2019.8737614"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ko, J.H., Na, T., Amir, M.F., and Mukhopadhyay, S. (2018, January 27\u201330). Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms. Proceedings of the 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Auckland, New Zealand.","DOI":"10.1109\/AVSS.2018.8639121"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, Z., Fan, K., Wang, S., Duan, L.Y., Lin, W., and Kot, A. (2019, January 21\u201325). Lossy Intermediate Deep Learning Feature Compression and Evaluation. Proceedings of the 27th ACM International Conference on Multimedia; Association for Computing Machinery, Nice, France.","DOI":"10.1145\/3343031.3350849"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Choi, H., and Baji\u0107, I.V. (2018, January 29\u201331). Near-Lossless Deep Feature Compression for Collaborative Intelligence. Proceedings of the 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP), Vancouver, BC, Canada.","DOI":"10.1109\/MMSP.2018.8547134"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Choi, H., and Baji\u0107, I.V. (2018, January 7\u201310). Deep Feature Compression for Collaborative Object Detection. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451100"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2017). ImageNet classification with deep convolutional neural networks. Commun. ACM, 60.","DOI":"10.1145\/3065386"},{"key":"ref_18","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2832","DOI":"10.1118\/1.2218316","article-title":"The impact of image information on compressibility and degradation in medical image compression","volume":"33","author":"Fidler","year":"2006","journal-title":"Med Phys."},{"key":"ref_20","unstructured":"Yanagihara, H. (1994). Image Compression Based on Pattern Fineness and Edge Presence. (5374958A), U.S. Patent, Available online: https:\/\/patents.google.com\/patent\/US5374958A\/en."},{"key":"ref_21","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_22","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2020, January 06). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Available online: tensorflow.org."},{"key":"ref_23","unstructured":"Anh, H.N. (2020, December 09). YOLO3 (Detection, Training, and Evaluation). Available online: https:\/\/github.com\/experiencor\/keras-yolo3."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (VOC) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_25","unstructured":"Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., and Zisserman, A. (2020, January 13). The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. Available online: http:\/\/www.pascal-network.org\/challenges\/VOC\/voc2012\/workshop\/index.html."},{"key":"ref_26","unstructured":"TechPowerUp (2021, January 15). AMD Ryzen 9 3900X Specs [Online]. Available online: https:\/\/www.techpowerup.com\/cpu-specs\/ryzen-9-3900x.c2128."},{"key":"ref_27","unstructured":"TechPowerUp (2021, January 15). NVIDIA GeForce RTX 2080 Specs [Online]. Available online: https:\/\/www.techpowerup.com\/gpu-specs\/geforce-rtx-2080.c3224."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1109\/TCSVT.2012.2221191","article-title":"Overview of the high efficiency video coding (HEVC) standard","volume":"22","author":"Sullivan","year":"2012","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sze, V., Budagavi, M., and Sullivan, G.J. (2014). High Efficiency Video Coding (HEVC): Algorithms and Architectures, Springer.","DOI":"10.1007\/978-3-319-06895-4"},{"key":"ref_30","first-page":"10","article-title":"Converting Video Formats with FFmpeg","volume":"2006","author":"Tomar","year":"2006","journal-title":"Linux J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/896\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:17:01Z","timestamp":1760159821000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/896"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,29]]},"references-count":31,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21030896"],"URL":"https:\/\/doi.org\/10.3390\/s21030896","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,29]]}}}