{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T20:59:45Z","timestamp":1781643585823,"version":"3.54.5"},"reference-count":39,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Institution of Information and Communications Technology Planning and Evaluation"},{"name":"Korean Government","award":["2020-0-01294"],"award-info":[{"award-number":["2020-0-01294"]}]},{"name":"Korean Government","award":["2021-0-00105"],"award-info":[{"award-number":["2021-0-00105"]}]},{"name":"Korean Government","award":["2021-0-00106"],"award-info":[{"award-number":["2021-0-00106"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Emerg. Sel. Topics Circuits Syst."],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1109\/jetcas.2023.3328863","type":"journal-article","created":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T18:07:11Z","timestamp":1698775631000},"page":"1094-1105","source":"Crossref","is-referenced-by-count":9,"title":["MiCE: An ANN-to-SNN Conversion Technique to Enable High Accuracy and Low Latency"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3470-835X","authenticated-orcid":false,"given":"Nguyen-Dong","family":"Ho","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Kyung Hee University, Yongin, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8871-8695","authenticated-orcid":false,"given":"Ik-Joon","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Kyung Hee University, Yongin, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1","article-title":"Optimal ANN-SNN conversion for high-accuracy and ultra-low-latency spiking neural networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Bu"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0788-3"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/211"},{"key":"ref4","volume-title":"Quantization (Signal Processing)","year":"2019"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2022.3214509"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.09.005"},{"key":"ref8","first-page":"1","article-title":"Optimal conversion of conventional artificial neural networks to spiking neural networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Deng"},{"key":"ref9","article-title":"Improved regularization of convolutional neural networks with cutout","author":"DeVries","year":"2017","journal-title":"arXiv:1708.04552"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2015.7280696"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58607-2_23"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01357"},{"key":"ref13","first-page":"1","article-title":"Bridging the gap between ANNs and SNNs by calibrating offset spikes","volume-title":"Proc. 11th Int. Conf. Learn. Represent.","author":"Hao"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref15","volume-title":"Algorithms a Nutshell","author":"Heineman","year":"2016"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18074.2021.9586266"},{"key":"ref17","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017","journal-title":"arXiv:1704.04861"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2003.820440"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(01)00078-8"},{"key":"ref20","first-page":"14945","article-title":"A unified optimization framework of ANN-SNN conversion: Towards optimal mapping from activation values to firing rates","volume-title":"Proc. 11th Int. Conf. Learn. Represent.","author":"Jiang"},{"key":"ref21","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2018.00435"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.918793"},{"key":"ref24","first-page":"6316","article-title":"A free lunch from ANN: Towards efficient, accurate spiking neural networks calibration","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref25","first-page":"1","article-title":"SGDR: Stochastic gradient descent with warm restarts","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Loshchilov"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.00535"},{"key":"ref27","first-page":"8024","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. NIPS","author":"Paszke"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.18-24-10464.1998"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3111897"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCC.2019.00020"},{"key":"ref31","article-title":"Theory and tools for the conversion of analog to spiking convolutional neural networks","author":"Rueckauer","year":"2016","journal-title":"arXiv:1612.04052"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2017.00682"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2019.00095"},{"key":"ref34","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Proc. 3rd Int. Conf. Learn. Represent.","author":"Simonyan"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2018.12.002"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2018.00331"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011311"},{"key":"ref39","first-page":"1","article-title":"Understanding straight-through estimator in training activation quantized neural nets","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Yin"}],"container-title":["IEEE Journal on Emerging and Selected Topics in Circuits and Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5503868\/10375869\/10302663.pdf?arnumber=10302663","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T01:48:49Z","timestamp":1705024129000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10302663\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12]]},"references-count":39,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/jetcas.2023.3328863","relation":{},"ISSN":["2156-3357","2156-3365"],"issn-type":[{"value":"2156-3357","type":"print"},{"value":"2156-3365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12]]}}}