{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T12:46:19Z","timestamp":1768740379930,"version":"3.49.0"},"reference-count":28,"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":"Challengeable Future Defense Technology Research and Development Program through the Agency for Defense Development"},{"name":"Defense Acquisition Program Administration (DAPA), in 2022","award":["915062201"],"award-info":[{"award-number":["915062201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3443406","type":"journal-article","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T17:39:59Z","timestamp":1723657199000},"page":"120326-120336","source":"Crossref","is-referenced-by-count":2,"title":["Dual-Core-Based Microcontrollers Inference Design and Performance Analysis"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0200-587X","authenticated-orcid":false,"given":"Dongchan","family":"Lee","sequence":"first","affiliation":[{"name":"Artificial Intelligence, University of Science and Technology (UST), Daejeon, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeong-Si","family":"Kim","sequence":"additional","affiliation":[{"name":"Electronics and Telecommunications Research Institute (ETRI), Daejeon, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0273-3542","authenticated-orcid":false,"given":"Seungtae","family":"Hong","sequence":"additional","affiliation":[{"name":"Artificial Intelligence, University of Science and Technology (UST), Daejeon, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Towards real smart apps: Investigating human-AI interactions in smartphone on-device AI apps","author":"Ching Yuen Siu","year":"2023","journal-title":"arXiv:2307.00756"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.iotcps.2023.02.004"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICTC49870.2020.9289190"},{"key":"ref4","volume-title":"STMicroelectronics AN5557 Application Note","year":"2022"},{"key":"ref5","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":"ref6","first-page":"517","article-title":"MicroNets: Neural network architectures for deploying TinyML applications on commodity microcontrollers","volume":"3","author":"Banbury","year":"2021","journal-title":"Proc. Mach. Learn. Syst."},{"key":"ref7","volume-title":"Microsoft","year":"2024"},{"key":"ref8","first-page":"11711","article-title":"MCUNet: Tiny deep learning on IoT devices","volume-title":"Proc. 34th Conf. Neural Inf. Process. Syst.","author":"Lin"},{"key":"ref9","article-title":"MCUNetV2: Memory-efficient patch-based inference for tiny deep learning","author":"Lin","year":"2021","journal-title":"arXiv:2110.15352"},{"key":"ref10","first-page":"22941","article-title":"On-device training under 256kb memory","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Lin"},{"key":"ref11","volume-title":"STM32Cube.AI, Your Software Tool To Optimize Artificial Neural Networks on STM32","year":"2024"},{"key":"ref12","volume-title":"Integrated Development Environment for STM32(STM32CubeIDE)","year":"2024"},{"key":"ref13","volume-title":"Stm32ai-modelzoo","year":"2024"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3007046"},{"key":"ref15","volume-title":"STM32Cube.AI Developer Cloud","year":"2024"},{"key":"ref16","volume-title":"Update: STM32Cube.AI and NVIDIA TAO Toolkit, Download and Watch a 10x Jump in Performance on an STM32H7 Running Vision AI","year":"2023"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3390\/s21092984"},{"key":"ref18","volume-title":"Tinyml: Machine Learning With Tensorflow Lite on Arduino and Ultra-low-power Microcontrollers","author":"Warden","year":"2019"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICTC58733.2023.10393661"},{"key":"ref20","volume-title":"Why and How to Get Started With Multicore Microcontrollers for IoT Devices At the Edge","author":"Beningo","year":"2024"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.3390\/en10030402"},{"key":"ref22","volume-title":"Using, the STM32 Chrom-ART Accelerator To Refresh an LCD-TFT Display","year":"2021"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1142\/9789814343459_others04"},{"key":"ref24","volume-title":"Stm32h747i-disco-bsp","year":"2024"},{"key":"ref25","volume-title":"OV5640 Datasheet","year":"2011"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2018.8451355"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1602.07360"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10380310\/10636162.pdf?arnumber=10636162","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,7]],"date-time":"2024-09-07T05:12:20Z","timestamp":1725685940000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10636162\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3443406","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}