{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T04:38:50Z","timestamp":1766378330351,"version":"3.37.3"},"reference-count":49,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/T026995\/1","EP\/V000497\/1"],"award-info":[{"award-number":["EP\/T026995\/1","EP\/V000497\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014013","name":"Soteria Project awarded by the U.K. Research and Innovation for the Digital Security by Design (DSbD) Programme","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014013","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Cal-Comp Electronic by the R&D project of the Cal-Comp Institute of Technology and Innovation"},{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior\u2014Brasil (CAPES-PROEX)\u2014Finance Code 001","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Amazonas State Research Support Foundation\u2014FAPEAM\u2014through the POSGRAD Project"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst."],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1109\/tcad.2023.3335313","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T19:37:34Z","timestamp":1700595454000},"page":"1121-1134","source":"Crossref","is-referenced-by-count":15,"title":["Counterexample Guided Neural Network Quantization Refinement"],"prefix":"10.1109","volume":"43","author":[{"given":"Jo\u00e3o Batista P.","family":"Matos","sequence":"first","affiliation":[{"name":"Graduate Program in Informatics, Federal University of Amazonas, Manaus, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8380-6058","authenticated-orcid":false,"given":"Eddie B.","family":"de Lima Filho","sequence":"additional","affiliation":[{"name":"R&#x0026;D Department, TPV Technology, Manaus, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6603-3476","authenticated-orcid":false,"given":"Iury","family":"Bessa","sequence":"additional","affiliation":[{"name":"Department of Electricity, Federal University of Amazonas, Manaus, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0028-5440","authenticated-orcid":false,"given":"Edoardo","family":"Manino","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Manchester, Manchester, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2612-6296","authenticated-orcid":false,"given":"Xidan","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Manchester, Manchester, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6235-4272","authenticated-orcid":false,"given":"Lucas C.","family":"Cordeiro","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Manchester, Manchester, U.K."}]}],"member":"263","reference":[{"key":"ref1","article-title":"End to end learning for self-driving cars","author":"Bojarski","year":"2016","journal-title":"arXiv:1604.07316"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2018.e00938"},{"key":"ref3","article-title":"A survey of model compression and acceleration for deep neural networks","author":"Cheng","year":"2017","journal-title":"arXiv:1710.09282"},{"key":"ref4","first-page":"2849","article-title":"Fixed point quantization of deep convolutional networks","volume-title":"Proc. 33rd Int. Conf. Mach. Learn.","author":"Lin"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1201\/9781003162810-13"},{"key":"ref6","first-page":"1","article-title":"Incremental network quantization: Towards lossless cnns with low-precision weights","volume-title":"Proc. 5th Int. Conf. Learn. Represent.","author":"Zhou"},{"key":"ref7","first-page":"1","article-title":"Deep compression: Compressing deep neural network with pruning, trained quantization and Huffman coding","volume-title":"Proc. 4th Int. Conf. Learn. Represent.","author":"Han"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1137\/22M1511709"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00286"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2020.100270"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58475-7_50"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI52525.2021.00035"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-15839-1_14"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-21222-2_3"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/DASC.2016.7778091"},{"volume-title":"The MNIST DATABASE of Handwritten Digits","author":"Yann","key":"ref16"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469-1809.1936.tb02137.x"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-13105-9_2"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-7566-5"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3049797.3049802"},{"key":"ref22","article-title":"Quantizing deep convolutional networks for efficient inference: A whitepaper","author":"Krishnamoorthi","year":"2018","journal-title":"arXiv:1806.08342"},{"key":"ref23","first-page":"784","article-title":"NEURODIFF: Scalable differential verification of neural networks using fine-grained approximation","volume-title":"Proc. 35th IEEE\/ACM Int. Conf. Autom. Softw. Eng. (ASE)","author":"Paulsen"},{"key":"ref24","first-page":"714","article-title":"ReluDiff: Differential verification of deep neural networks","volume-title":"Proc. ISCE","author":"Paulsen"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-00768-2_16"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-78800-3_24"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30942-8_39"},{"key":"ref28","article-title":"A white paper on neural network quantization","author":"Nagel","year":"2021","journal-title":"arXiv:2106.08295"},{"key":"ref29","article-title":"Verifying quantized neural networks using SMT-based model checking","author":"Sena","year":"2021","journal-title":"arXiv:2106.05997"},{"key":"ref30","article-title":"Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or \u22121","author":"Courbariaux","year":"2016","journal-title":"arXiv:1602.02830"},{"key":"ref31","article-title":"Model compression as constrained optimization, with application to neural nets. Part II: Quantization","author":"Carreira-Perpi\u00f1\u00e1n","year":"2017","journal-title":"arXiv:1707.04319"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207413"},{"issue":"1","key":"ref33","first-page":"1","article-title":"A greedy algorithm for quantizing neural networks","volume":"22","author":"Lybrand","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11663"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00748"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00222"},{"key":"ref37","article-title":"Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients","author":"Zhou","year":"2016","journal-title":"arXiv:1606.06160"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3549535"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-37703-7_20"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-45237-7_5"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16496"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2022.3197697"},{"key":"ref43","article-title":"QNNVerifier: A tool for verifying neural networks using SMT-based model checking","author":"Song","year":"2021","journal-title":"arXiv:2111.13110"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/3238147.3240481"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1002\/stvr.1793"},{"key":"ref46","article-title":"The second international verification of neural networks competition (VNN-COMP 2021): Summary and results","author":"Bak","year":"2021","journal-title":"arXiv:2109.00498"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/4235.996017"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-63387-9_5"},{"key":"ref49","article-title":"Towards efficient training for neural network quantization","author":"Jin","year":"2019","journal-title":"arXiv:1912.10207"}],"container-title":["IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/43\/10477239\/10324349.pdf?arnumber=10324349","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T22:35:10Z","timestamp":1733956510000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10324349\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":49,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tcad.2023.3335313","relation":{},"ISSN":["0278-0070","1937-4151"],"issn-type":[{"type":"print","value":"0278-0070"},{"type":"electronic","value":"1937-4151"}],"subject":[],"published":{"date-parts":[[2024,4]]}}}