{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T23:56:48Z","timestamp":1772927808006,"version":"3.50.1"},"reference-count":32,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Fundamentals"],"published-print":{"date-parts":[[2026,3,1]]},"DOI":"10.1587\/transfun.2025vlp0004","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T22:06:13Z","timestamp":1758665173000},"page":"581-589","source":"Crossref","is-referenced-by-count":0,"title":["TGSyn: Transformer Guided Topology-based Exact Synthesis for Majority-Inverter Graphs"],"prefix":"10.1587","volume":"E109.A","author":[{"given":"Xianliange","family":"GE","sequence":"first","affiliation":[{"name":"Graduate School of Information, Production and System, Waseda University"}]},{"given":"Shinichi","family":"NISHIZAWA","sequence":"additional","affiliation":[{"name":"Graduate School of Information, Production and System, Waseda University"}]},{"given":"Shinji","family":"KIMURA","sequence":"additional","affiliation":[{"name":"Graduate School of Information, Production and System, Waseda University"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] L. Amar\u00fa, P.E. Gaillardon, and G. De Micheli, \u201cMajority-inverter graph: A new paradigm for logic optimization,\u201d IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst., vol.35, no.5, pp.806-819, 2016. 10.1109\/tcad.2015.2488484","DOI":"10.1109\/TCAD.2015.2488484"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] M. Soeken, L.G. Amaru, P.E. Gaillardon, and G. De Micheli, \u201cExact synthesis of majority-inverter graphs and its applications,\u201d IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst., vol.36, no.11, pp.1842-1855, 2017. 10.1109\/tcad.2017.2664059","DOI":"10.1109\/TCAD.2017.2664059"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] W. Haaswijk, M. Soeken, A. Mishchenko, and G. De Micheli, \u201cSat-based exact synthesis: Encodings, topology families, and parallelism,\u201d IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst., vol.39, no.4, pp.871-884, 2019. 10.1109\/tcad.2019.2897703","DOI":"10.1109\/TCAD.2019.2897703"},{"key":"4","unstructured":"[4] D.E. Knuth, The Art of Computer Programming, Volume 4, Fascicle 6: Satisfiability, Addison-Wesley Professional, 2015."},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] W. Haaswijk, M. Soeken, A. Mishchenko, and G. De Micheli, \u201cSAT based exact synthesis using DAG topology families,\u201d 2018 55Th ACM\/ESDA\/IEEE Design Automation Conference (DAC), pp.1-6, IEEE, 2018. 10.1109\/dac.2018.8465888","DOI":"10.1109\/DAC.2018.8465888"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] X. Ge and S. Kimura, \u201cTopology-based exact synthesis for majority inverter graph,\u201d 2022 IEEE International Symposium on Circuits and Systems (ISCAS), pp.3255-3259, 2022. 10.1109\/iscas48785.2022.9937527","DOI":"10.1109\/ISCAS48785.2022.9937527"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] X. Ge and S. Kimura, \u201cTheory and application of topology-based exact synthesis for majority-inverter graphs,\u201d IEICE Trans. Fundamentals, vol.E106-A, no.9, pp.1241-1250, Sept. 2023. 10.1587\/transfun.2022eap1103","DOI":"10.1587\/transfun.2022EAP1103"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P.V. Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T.L. Scao, S. Gugger, M. Drame, Q. Lhoest, and A. Rush, \u201cTransformers: State-of-the-art natural language processing,\u201d Proc. 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp.38-45, 2020. 10.18653\/v1\/2020.emnlp-demos.6","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] S. Khan, M. Naseer, M. Hayat, S.W. Zamir, F.S. Khan, and M. Shah, \u201cTransformers in vision: A survey,\u201d ACM Comput. Surv. (CSUR), vol.54, no.10s, pp.1-41, 2022. 10.1145\/3505244","DOI":"10.1145\/3505244"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] R.L. Wigington, \u201cA new concept in computing,\u201d Proc. IRE, vol.47, no.4, pp.516-523, 1959. 10.1109\/jrproc.1959.287311","DOI":"10.1109\/JRPROC.1959.287311"},{"key":"11","unstructured":"[11] F. Brglez, \u201cA neutral netlist of 10 combinational benchmark circuits and a target translator in fortran,\u201d Proc. Intl. Symp. Circuits and Systems, 1985."},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] V. Kabanets and J.Y. Cai, \u201cCircuit minimization problem,\u201d Proc. Thirty-Second Annual ACM Symposium on Theory of Computing, pp.73-79, 2000. 10.1145\/335305.335314","DOI":"10.1145\/335305.335314"},{"key":"13","unstructured":"[13] R. Ilango, B. Loff, and I.C. Oliveira, \u201cNp-hardness of circuit minimization for multi-output functions,\u201d Proc. 35th Computational Complexity Conference (CCC\u201920), pp.1-36, 2020. 10.4230\/LIPIcs.CCC.2020.22"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] H. Riener, W. Haaswijk, A. Mishchenko, G. De Micheli, and M. Soeken, \u201cOn-the-fly and dag-aware: Rewriting boolean networks with exact synthesis,\u201d 2019 Design, Automation &amp; Test in Europe Conference &amp; Exhibition (DATE), pp.1649-1654, IEEE, 2019. 10.23919\/date.2019.8715185","DOI":"10.23919\/DATE.2019.8715185"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] H. Riener, A. Mishchenko, and M. Soeken, \u201cExact dag-aware rewriting,\u201d 2020 Design, Automation &amp; Test in Europe Conference &amp; Exhibition (DATE), pp.732-737, IEEE, 2020. 10.23919\/date48585.2020.9116379","DOI":"10.23919\/DATE48585.2020.9116379"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] J.A. Gallian, \u201cA dynamic survey of graph labeling,\u201d Electronic Journal of Combinatorics, vol.1, no.DynamicSurveys, p.DS6, 2018.","DOI":"10.37236\/27"},{"key":"17","unstructured":"[17] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, \u0141. Kaiser, and I. Polosukhin, \u201cAttention is all you need,\u201d Advances in Neural Information Processing Systems, vol.30, 2017."},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] D.E. Rumelhart, G.E. Hinton, R.J. Williams, and J. Ronald, \u201cLearning internal representations by error propagation,\u201d ICS Report 8506, 1985. 10.21236\/ADA164453","DOI":"10.21236\/ADA164453"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] S. Hochreiter and J. Schmidhuber, \u201cLong short-term memory,\u201d Neural Computation, vol.9, no.8, pp.1735-1780, 1997. 10.1162\/neco.1997.9.8.1735","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"20","unstructured":"[20] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, \u201cEmpirical evaluation of gated recurrent neural networks on sequence modeling,\u201d arXiv preprint arXiv:1412.3555, 2014. 10.48550\/arXiv.1412.3555"},{"key":"21","unstructured":"[21] J. Gehring, M. Auli, D. Grangier, D. Yarats, and Y.N. Dauphin, \u201cConvolutional sequence to sequence learning,\u201d International Conference on Machine Learning, pp.1243-1252, PMLR, 2017."},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] K. Fukushima, \u201cCognitron: A self-organizing multilayered neural network,\u201d Biol. Cybernetics, vol.20, no.3-4, pp.121-136, 1975. 10.1007\/bf00342633","DOI":"10.1007\/BF00342633"},{"key":"23","unstructured":"[23] S. Ioffe and C. Szegedy, \u201cBatch normalization: Accelerating deep network training by reducing internal covariate shift,\u201d International Conference on Machine Learning, pp.448-456, PMLR, 2015."},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] I.J. Good, \u201cRational decisions,\u201d Journal of the Royal Statistical Society: Series B (Methodological), vol.14, no.1, pp.107-114, 1952. 10.1111\/j.2517-6161.1952.tb00104.x","DOI":"10.1111\/j.2517-6161.1952.tb00104.x"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] P. Domingos, \u201cA few useful things to know about machine learning,\u201d Commun. ACM, vol.55, no.10, pp.78-87, 2012. 10.1145\/2347736.2347755","DOI":"10.1145\/2347736.2347755"},{"key":"26","unstructured":"[26] T. Yu and H. Zhu, \u201cHyper-parameter optimization: A review of algorithms and applications,\u201d arXiv preprint arXiv:2003.05689, 2020. 10.48550\/arXiv.2003.05689"},{"key":"27","unstructured":"[27] J. Bergstra, R. Bardenet, Y. Bengio, and B. K\u00e9gl, \u201cAlgorithms for hyper-parameter optimization,\u201d Advances in Neural Information Processing Systems, vol.24, 2011."},{"key":"28","unstructured":"[28] Microsoft, \u201cNeural network intelligence,\u201d Jan. 2021."},{"key":"29","unstructured":"[29] L. Amar\u00fa, P.E. Gaillardon, and G. De Micheli, \u201cThe EPFL combinational benchmark suite,\u201d Proc. 24th International Workshop on Logic &amp; Synthesis (IWLS), 2015."},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] L. Breiman, \u201cRandom forests,\u201d Machine Learning, vol.45, pp.5-32, 2001. 10.1023\/a:1010933404324","DOI":"10.1023\/A:1010933404324"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] Y. Freund and R.E. Schapire, \u201cA decision-theoretic generalization of on-line learning and an application to boosting,\u201d Journal of Computer and System Sciences, vol.55, no.1, pp.119-139, 1997. 10.1006\/jcss.1997.1504","DOI":"10.1006\/jcss.1997.1504"},{"key":"32","doi-asserted-by":"publisher","unstructured":"[32] J. Zhu, H. Zou, S. Rosset, and T. Hastie, \u201cMulti-class adaboost,\u201d Statistics and its Interface, vol.2, no.3, pp.349-360, 2009. 10.4310\/sii.2009.v2.n3.a8","DOI":"10.4310\/SII.2009.v2.n3.a8"}],"container-title":["IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E109.A\/3\/E109.A_2025VLP0004\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T04:11:46Z","timestamp":1772856706000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E109.A\/3\/E109.A_2025VLP0004\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,1]]},"references-count":32,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.1587\/transfun.2025vlp0004","relation":{},"ISSN":["0916-8508","1745-1337"],"issn-type":[{"value":"0916-8508","type":"print"},{"value":"1745-1337","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,1]]},"article-number":"2025VLP0004"}}