{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T13:58:34Z","timestamp":1783605514446,"version":"3.55.0"},"reference-count":61,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"armasuisse Science and Technology"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3404878","type":"journal-article","created":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T18:14:11Z","timestamp":1716488051000},"page":"75093-75107","source":"Crossref","is-referenced-by-count":17,"title":["Flexible and Fully Quantized Lightweight TinyissimoYOLO for Ultra-Low-Power Edge Systems"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0283-0031","authenticated-orcid":false,"given":"Julian","family":"Moosmann","sequence":"first","affiliation":[{"name":"Department of Information Technology and Electrical Engineering, ETH Zürich, Zürich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4942-6673","authenticated-orcid":false,"given":"Hanna","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"Department of Information Technology and Electrical Engineering, ETH Zürich, Zürich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9105-0139","authenticated-orcid":false,"given":"Nicky","family":"Zimmerman","sequence":"additional","affiliation":[{"name":"IDSIA,Università della Svizzera Italiana, Lugano, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8875-7611","authenticated-orcid":false,"given":"Georg","family":"Rutishauser","sequence":"additional","affiliation":[{"name":"Department of Information Technology and Electrical Engineering, ETH Zürich, Zürich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8068-3806","authenticated-orcid":false,"given":"Luca","family":"Benini","sequence":"additional","affiliation":[{"name":"Department of Information Technology and Electrical Engineering, ETH Zürich, Zürich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0368-8923","authenticated-orcid":false,"given":"Michele","family":"Magno","sequence":"additional","affiliation":[{"name":"Department of Information Technology and Electrical Engineering, ETH Zürich, Zürich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2971654"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2022.3206782"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/access.2018.2793265"},{"key":"ref4","volume-title":"Number of Internet of Things (IoT) Connected Devices Worldwide From 2019 to 2023, With Forecasts From 2022 to 2030 (in Billions) [Graph]","year":"2019"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3560905.3568520"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2939201"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52729.2023.01385"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-023-01748-7"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2016.91"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2017.690"},{"key":"ref13","article-title":"YOLOV3: An incremental improvement","author":"Redmon","year":"2018","journal-title":"arXiv:1804.02767"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2004.10934"},{"key":"ref15","volume-title":"YOLOV5 By Ultralytics","author":"Jocher","year":"2020"},{"key":"ref16","article-title":"YOLOv6: A single-stage object detection framework for industrial applications","author":"Li","year":"2022","journal-title":"arXiv:2209.02976"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52729.2023.00721"},{"key":"ref18","volume-title":"YOLO By Ultralytics","author":"Jocher","year":"2023"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/jsen.2022.3210773"},{"key":"ref20","volume-title":"TinyML: Machine Learning With Tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers","author":"Warden","year":"2019"},{"key":"ref21","volume-title":"Neural Network Intelligence","year":"2021"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/aicas57966.2023.10168657"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2021.3091643"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/iscas51556.2021.9401730"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2017.322"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2015.7298965"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/lra.2022.3223556"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/iros55552.2023.10341595"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/3dv.2018.00015"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/icra40945.2020.9196885"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/access.2022.3157626"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.3390\/app10020497"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2022.01.135"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/icra48506.2021.9561675"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0733-5"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/icecs202256217.2022.9970956"},{"key":"ref37","first-page":"1","article-title":"Sparse: Sparse architecture search for CNNs on resource-constrained microcontrollers","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Fedorov"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/tc.2022.3177955"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3437984.3458836"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/592"},{"key":"ref41","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding","author":"Han","year":"2015","journal-title":"arXiv:1510.00149"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.07.045"},{"key":"ref43","first-page":"800","article-title":"TensorFlow lite micro: Embedded machine learning for TinyML systems","volume":"3","author":"David","year":"2021","journal-title":"Proc. Mach. Learn. Syst."},{"key":"ref44","first-page":"1935","article-title":"Resource-efficient machine learning in 2 KB RAM for the Internet of Things","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Kumar"},{"key":"ref45","article-title":"CMSIS-NN: Efficient neural network kernels for ARM cortex-M CPUs","author":"Lai","year":"2018","journal-title":"arXiv:1801.06601"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/icfsp55781.2022.9924667"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/aicas54282.2022.9870017"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/3560905.3568300"},{"key":"ref49","first-page":"11711","article-title":"MCUNet: Tiny deep learning on IoT devices","volume-title":"Proc. 34th Conf. Neural Inf. Process. Syst.","author":"Lin"},{"key":"ref50","first-page":"2346","article-title":"Memory-efficient patch-based inference for tiny deep learning","volume-title":"Proc. 35th Conf. Neural Inf. Process. Syst.","author":"Lin"},{"key":"ref51","article-title":"MCUNetV2: Memory-efficient patch-based inference for tiny deep learning","author":"Lin","year":"2021","journal-title":"arXiv:2110.15352"},{"key":"ref52","first-page":"326","article-title":"Memory-driven mixed low precision quantization for enabling deep network inference on microcontrollers","volume":"2","author":"Rusci","year":"2020","journal-title":"Proc. Mach. Learn. Syst."},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/AICAS57966.2023.10168577"},{"key":"ref54","article-title":"Learned step size quantization","author":"Esser","year":"2019","journal-title":"arXiv:1902.08153"},{"key":"ref55","article-title":"QuantLab: A modular framework for training and deploying mixed-precision NNs","volume-title":"TinyML Summit","author":"Spallanzani","year":"2022"},{"key":"ref56","first-page":"112","article-title":"Trained quantization thresholds for accurate and efficient fixed-point inference of deep neural networks","volume":"2","author":"Jain","year":"2020","journal-title":"Proc. Mach. Learn. Syst."},{"key":"ref57","article-title":"A survey of quantization methods for efficient neural network inference","author":"Gholami","year":"2021","journal-title":"arXiv:2103.13630"},{"key":"ref58","first-page":"11875","article-title":"HAWQV3: Dyadic neural network quantization","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","volume":"139","author":"Yao"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/jssc.2021.3114881"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/jssc.2023.3318301"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/tc.2021.3066883"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10380310\/10538092.pdf?arnumber=10538092","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T17:43:56Z","timestamp":1717177436000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10538092\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":61,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3404878","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}