{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T18:08:15Z","timestamp":1789322895798,"version":"build-2803163510"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","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"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Emerg. Topics Comput."],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1109\/tetc.2024.3435135","type":"journal-article","created":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T17:28:03Z","timestamp":1722619683000},"page":"512-524","source":"Crossref","is-referenced-by-count":5,"title":["CAT SNN: Conversion Aware Training for High Accuracy and Hardware Friendly Spiking Neural Networks"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8631-6804","authenticated-orcid":false,"given":"Dongwoo","family":"Lew","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Korea University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3251-0024","authenticated-orcid":false,"given":"Jongsun","family":"Park","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Korea University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1677-2"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2015.00099"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2017.12.005"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ijcnn.2019.8852380"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2016.00508"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011311"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2018.00331"},{"key":"ref8","article-title":"Optimal ANN-SNN conversion for high-accuracy and ultra-low-latency spiking neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Bu","year":"2022"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18072.2020.9218689"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2019.00095"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2017.00682"},{"key":"ref12","first-page":"6316","article-title":"A free lunch from ANN: Towards efficient, accurate spiking neural networks calibration","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Li","year":"2021"},{"key":"ref13","article-title":"Optimal conversion of conventional artificial neural networks to spiking neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Deng","year":"2021"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ijcnn.2015.7280696"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/mm.2018.112130359"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/tcad.2015.2474396"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/isca45697.2020.00038"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/jssc.2020.2970709"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2018.00286"},{"key":"ref20","article-title":"PACT: Parameterized clipping activation for quantized neural networks","author":"Choi","year":"2018"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3489517.3530422"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1142\/s0129065720500276"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/tbcas.2022.3181808"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90020-8"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2019.06.004"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1201\/b17103-3"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2016.90"},{"key":"ref28","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108002"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2022.06.001"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/dac18074.2021.9586266"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/tbcas.2018.2880425"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3240765.3240803"},{"key":"ref34","article-title":"Convolutional neural networks using logarithmic data representation","author":"Miyashita","year":"2016"},{"key":"ref35","first-page":"41","article-title":"Fine-grained DRAM: Energy-efficient DRAM for extreme bandwidth systems","volume-title":"Proc. 50th Annu. IEEE\/ACM Int. Symp. Microarchitecture","author":"O\u2019Connor"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080246"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1604850113"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i15.29587"},{"key":"ref39","article-title":"Learning Delays in spiking neural networks using dilated convolutions with learnable spacings","author":"Hammouamri","year":"2023"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3263008"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2019.00065"},{"key":"ref42","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Tan","year":"2024"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3317870"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ted.2021.3089987"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/3489517.3530457"}],"container-title":["IEEE Transactions on Emerging Topics in Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6245516\/11045241\/10622017.pdf?arnumber=10622017","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T06:15:42Z","timestamp":1750745742000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10622017\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":45,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tetc.2024.3435135","relation":{},"ISSN":["2168-6750","2376-4562"],"issn-type":[{"value":"2168-6750","type":"electronic"},{"value":"2376-4562","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4]]}}}