{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T08:18:35Z","timestamp":1776154715143,"version":"3.50.1"},"reference-count":43,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the Framework of \u201cBAYERN DIGITAL II,\u201d"},{"name":"Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)","award":["524986327"],"award-info":[{"award-number":["524986327"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst."],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1109\/tcad.2024.3484354","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T13:20:01Z","timestamp":1729516801000},"page":"1250-1261","source":"Crossref","is-referenced-by-count":11,"title":["On-Device Training of Fully Quantized Deep Neural Networks on Cortex-M Microcontrollers"],"prefix":"10.1109","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8932-5212","authenticated-orcid":false,"given":"Mark","family":"Deutel","sequence":"first","affiliation":[{"name":"Department of Computer Science, Friedrich-Alexander-Universit&#x00E4;t Erlangen-N&#x00FC;rnberg, Erlangen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3663-6484","authenticated-orcid":false,"given":"Frank","family":"Hannig","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Friedrich-Alexander-Universit&#x00E4;t Erlangen-N&#x00FC;rnberg, Erlangen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8108-0230","authenticated-orcid":false,"given":"Christopher","family":"Mutschler","sequence":"additional","affiliation":[{"name":"Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6285-5862","authenticated-orcid":false,"given":"J\u00fcrgen","family":"Teich","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Friedrich-Alexander-Universit&#x00E4;t Erlangen-N&#x00FC;rnberg, Erlangen, Germany"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MCAS.2023.3302182"},{"key":"ref2","first-page":"11711","article-title":"MCUNet: Tiny deep learning on IoT devices","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lin"},{"key":"ref3","first-page":"1","article-title":"Energy-efficient deployment of deep learning applications on cortex-M based microcontrollers using deep compression","volume-title":"Proc. MBMV 26th Workshop","author":"Deutel"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-658-45018-2_4"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00286"},{"key":"ref6","first-page":"1","article-title":"Memory-efficient backpropagation through time","volume-title":"Proc. 30th Adv. Neural Inf. Process. Syst.","author":"Gruslys"},{"key":"ref7","article-title":"Training deep nets with sublinear memory cost","author":"Chen","year":"2016","journal-title":"arXiv:1604.06174"},{"key":"ref8","first-page":"1","article-title":"Dynamic sparse graph for efficient deep learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Liu"},{"key":"ref9","first-page":"1","article-title":"E2-train: Training state-of-the-art CNNs with over 80% energy savings","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref10","first-page":"1","article-title":"Training deep neural networks with 8-bit floating point numbers","volume-title":"Proc. 32nd Adv. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref11","first-page":"22941","article-title":"On-device training under 256KB memory","volume-title":"Proc. 36th Adv. Neural Inf. Process. Syst.","author":"Lin"},{"key":"ref12","first-page":"11285","article-title":"TinyTL: Reduce memory, not parameters for efficient on-device learning","volume-title":"Proc. 34th Adv. Neural Inf. Process. Syst.","author":"Cai"},{"key":"ref13","first-page":"1","article-title":"Training BatchNorm and only BatchNorm: On the expressive power of random features in CNNs","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Frankle"},{"key":"ref14","first-page":"1","article-title":"K for the price of 1: Parameter-efficient multi-task and transfer learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Mudrakarta"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533927"},{"key":"ref16","first-page":"17573","article-title":"POET: Training neural networks on tiny devices with integrated rematerialization and paging","volume-title":"Proc. 39th Int. Conf. Mach. Learn.","author":"Patil"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3498361.3538928"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3495243.3560545"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3498361.3539765"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/SEC.2018.00047"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/s0079-7421(08)60536-8"},{"key":"ref22","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2017","journal-title":"arXiv:1412.6980"},{"key":"ref23","volume-title":"Neural Networks for Machine Learning Lecture 6a Overview of Mini-Batch Gradient Descent","author":"Hinton","year":"2024"},{"key":"ref24","volume-title":"Bearing Data Center","year":"2024"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0075196"},{"key":"ref26","article-title":"Speech commands: A dataset for limited-vocabulary speech recognition","author":"Warden","year":"2018","journal-title":"arXiv:1804.03209"},{"key":"ref27","volume-title":"Animals-10","year":"2024"},{"key":"ref28","volume-title":"Learning Multiple Layers of Features From Tiny Images","author":"Krizhevsky","year":"2009"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref32","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017","journal-title":"arXiv:1708.07747"},{"key":"ref33","article-title":"Deep learning for classical Japanese literature","author":"Clanuwat","year":"2018","journal-title":"arXiv:1812.01718"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"ref35","first-page":"1","article-title":"Binarized neural networks","volume-title":"Proc. 30th Adv. Neural Inf. Process. Syst.","author":"Hubara"},{"key":"ref36","article-title":"Quantizing deep convolutional networks for efficient inference: A whitepaper","author":"Krishnamoorthi","year":"2018","journal-title":"arXiv:1806.08342"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.761"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01168-2"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2013.77"},{"key":"ref40","article-title":"Caltech-UCSD birds 200","author":"Welinder","year":"2010"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10599-4_29"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248092"},{"key":"ref43","article-title":"Visual wake words dataset","author":"Chowdhery","year":"2019","journal-title":"arXiv:1906.05721"}],"container-title":["IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/43\/10934961\/10726519.pdf?arnumber=10726519","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T18:43:13Z","timestamp":1763750593000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10726519\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":43,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tcad.2024.3484354","relation":{},"ISSN":["0278-0070","1937-4151"],"issn-type":[{"value":"0278-0070","type":"print"},{"value":"1937-4151","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4]]}}}