{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T14:48:14Z","timestamp":1777387694949,"version":"3.51.4"},"reference-count":24,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Fundamentals"],"published-print":{"date-parts":[[2021,11,1]]},"DOI":"10.1587\/transfun.2020kep0016","type":"journal-article","created":{"date-parts":[[2021,5,16]],"date-time":"2021-05-16T22:06:40Z","timestamp":1621202800000},"page":"1477-1487","source":"Crossref","is-referenced-by-count":3,"title":["Neural Network Calculations at the Speed of Light Using Optical Vector-Matrix Multiplication and Optoelectronic Activation"],"prefix":"10.1587","volume":"E104.A","author":[{"given":"Naoki","family":"HATTORI","sequence":"first","affiliation":[{"name":"Nagoya University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"SHIOMI","sequence":"additional","affiliation":[{"name":"Kyoto University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yutaka","family":"MASUDA","sequence":"additional","affiliation":[{"name":"Nagoya University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tohru","family":"ISHIHARA","sequence":"additional","affiliation":[{"name":"Nagoya University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akihiko","family":"SHINYA","sequence":"additional","affiliation":[{"name":"NTT Nanophotonics Center"},{"name":"NTT Basic Research Laboratories"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masaya","family":"NOTOMI","sequence":"additional","affiliation":[{"name":"NTT Nanophotonics Center"},{"name":"NTT Basic Research Laboratories"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] T. Ishihara, J. Shiomi, N. Hattori, Y. Masuda, A. Shinya, and M. Notomi, \u201cAn optical neural network architecture based on highly parallelized WDM-multiplier-accumulator,\u201d Proc. IEEE\/ACM Workshop on Photonics-Optics Technology Oriented Networking, Information and Computing Systems, pp.15-21, Nov. 2019. 10.1109\/photonics49561.2019.00008","DOI":"10.1109\/PHOTONICS49561.2019.00008"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] A. Ceyhan, M. Jung, S. Panth, S.K. Lim, and A. Naeemi, \u201cImpact of size effects in local interconnects for future technology nodes: A study based on full-chip layouts,\u201d Proc. IEEE Interconnect Technology Conference\/Advanced Metallization Conference, pp.345-348, May 2014. 10.1109\/iitc.2014.6831831","DOI":"10.1109\/IITC.2014.6831831"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] Y. Cao, Predictive Technology Model for Robust Nanoelectronic Design, Springer, New York, NY, 2011. 10.1007\/978-1-4614-0445-3","DOI":"10.1007\/978-1-4614-0445-3"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] S. Sinha, B. Cline, G. Yeric, V. Chandra, and Y. Cao, \u201cDesign benchmarking to 7nm with FinFET predictive technology models,\u201d Proc. International Symposium on Low Power Electronics and Design, pp.15-20, July 2012. 10.1145\/2333660.2333666","DOI":"10.1145\/2333660.2333666"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] X. Wu, J. Xu, Y. Ye, Z. Wang, M. Nikdast, and X. Wang, \u201cSUOR: Sectioned undirectional optical ring for chip multiprocessor,\u201d J. Emerg. Technol. Comput. Syst., vol.10, no.4, pp.1-25, April 2014. 10.1145\/2600072","DOI":"10.1145\/2600072"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] A.N. Tait, T.F. de Lima, E. Zhou, A.X. Wu, M.A. Nahmias, B.J. Shastri, and P.R. Prucnal, \u201cNeuromorphic photonic networks using silicon photonic weight banks,\u201d Scientific Reports, vol.7, no.1, Aug. 2017. 10.1038\/s41598-017-07754-z","DOI":"10.1038\/s41598-017-07754-z"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] W. Liu, W. Liu, Y. Ye, Q. Lou, Y. Xie, and L. Jiang, \u201cHolyLight: A nanophotonic accelerator for deep learning in data centers,\u201d Proc. Design Automation, and Test in Europe, pp.1483-1488, March 2019. 10.23919\/date.2019.8715195","DOI":"10.23919\/DATE.2019.8715195"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] N. Janosik, Q. Cheng, M. Glick, Y. Huang, and K. Bergman, \u201cHigh-resolution silicon microring based architecture for optical matrix multiplication,\u201d Proc. Conference on Lasers and Electro-Optics, no.SM2J.3, May 2019. 10.1364\/cleo_si.2019.sm2j.3","DOI":"10.1364\/CLEO_SI.2019.SM2J.3"},{"key":"9","unstructured":"[9] M.B. On, H. Lu, H. Chen, R. Proietti, and S.J.B. Yoo, \u201cWavelength-space domain high-throughput artificial neural networks by parallel photoelectric matrix multiplier,\u201d Proc. IEEE Optical Fiber Communications Conference and Exhibition, pp.1-3, March 2020. 10.1364\/ofc.2020.w3a.2"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] Y. Shen, N.C. Harris, S. Skirlo, M. Prabhu, T.B.-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund, and M. Soljacic, \u201cDeep learning with coherent nanophotonic circuits,\u201d Nature, vol.11, no.7, pp.441-446, June 2017. 10.1038\/nphoton.2017.93","DOI":"10.1038\/nphoton.2017.93"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] J. Gu, Z. Zhao, C. Feng, Z. Ying, M. Liu, R.T. Chen, and D.Z. Pan, \u201cTowards hardware-efficient optical neural networks: Beyond FFT architecture via joint learnability,\u201d IEEE Trans. Computer-Aided Des. Integr. Circuits Syst., vol.40, no.9, pp.1796-1809, 2021. 10.1109\/tcad.2020.3027649","DOI":"10.1109\/TCAD.2020.3027649"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] S. Kita, K. Nozaki, K. Takata, A. Shinya, and M. Notomi, \u201cUltrashort low-loss \u03c8 gates for linear optical logic on Si photonics platform,\u201d Commun. Phys., vol.3, no.33, March 2020. 10.1038\/s42005-020-0298-2","DOI":"10.1038\/s42005-020-0298-2"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] R. Hamerly, L. Bernstein, A. Sludds, M. Soljacic, and D. Englund, \u201cLarge-scale optical neural networks based on photoelectric multiplication,\u201d Phys. Rev. X, vol.9, no.2, pp.21-32, May 2019. 10.1103\/physrevx.9.021032","DOI":"10.1103\/PhysRevX.9.021032"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] Q. Xu, B. Schmidt, J. Shakya, and M. Lipson, \u201cCascaded silicon micro-ring Modulators for WDM optical interconnection,\u201d Opt. Express, vol.14, no.20, pp.9431-9436, Oct. 2006. 10.1364\/oe.14.009431","DOI":"10.1364\/OE.14.009431"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] S. Chen, Y. Shi, S. He, and D. Dai, \u201cLow-loss and broadband 2\u00d72 silicon thermooptic Mach-Zehnder switch with bent directional couplers,\u201d Opt. Lett., vol.41, no.4, pp.836-839, Feb. 2016. 10.1364\/ol.41.000836","DOI":"10.1364\/OL.41.000836"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] K. Nozaki, S. Matsuo, T. Fujii, K. Takeda, A. Shinya, E. Kuramochi, and M. Notomi, \u201cFemtofarad optoelectronic integration demonstrating energy-saving signal conversion and nonlinear functions,\u201d Nature Photonics, vol.13, pp.454-459, July 2019. 10.1038\/s41566-019-0397-3","DOI":"10.1038\/s41566-019-0397-3"},{"key":"17","unstructured":"[17] V. Nair and G.E. Hinton, \u201cRectified linear units improve restricted Boltzmann machines,\u201d Proc. ICML'10, pp.807-814, 2010."},{"key":"18","unstructured":"[18] D.-A. Clevert, T. Unterthiner, and S. Hochreiter, \u201cFast and accurate deep network learning by exponential linear units (ELUs),\u201d arXiv preprint arXiv:1511.07289, 2015."},{"key":"19","unstructured":"[19] A.L. Maas, A.Y. Hannun, and A.Y. Ng, \u201cRectifier nonlinearities improve neural network acoustic models,\u201d Proc. ICML, vol.30, no.1, Citeseer, p.3, 2013."},{"key":"20","unstructured":"[20] S. Ioffe and C. Szegedy, \u201cBatch normalization: Accelerating deep network training by reducing internal covariate shift,\u201d Proc. Int'l Conference on Machine Learning, pp.448-456, Dec. 2015."},{"key":"21","unstructured":"[21] Y. Lecun, \u201cThe mnist database of handwritten digits,\u201d 1998. [Online]. Available: http:\/\/yann.lecun.com\/exdb\/mnist\/"},{"key":"22","unstructured":"[22] M. Courbariaux, Y. Bengio, and J.-P. David, \u201cBinaryConnect: Training deep neural networks with binary weights during propagations,\u201d Proc. Int'l Conference on Neural Information Processing Systems, pp.3123-3131, Dec. 2015."},{"key":"23","unstructured":"[23] D.P. Kingma and J. Ba, \u201cAdam: A method for stochastic optimization,\u201d arXiv preprint arXiv:1412.6980, 2014."},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] X. Ming, H. Zunkai, T. Li, W. Ning, Z. Yongxin, W. Hui, and F. Songlin, \u201cAn area-efficient 10-Bit buffer-reused DAC for AMOLED column driver ICs,\u201d Electronics, vol.9, no.2, p.208, Sept. 2020. 10.3390\/electronics9020208","DOI":"10.3390\/electronics9020208"}],"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\/E104.A\/11\/E104.A_2020KEP0016\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T04:40:40Z","timestamp":1715143240000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E104.A\/11\/E104.A_2020KEP0016\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,1]]},"references-count":24,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2021]]}},"URL":"https:\/\/doi.org\/10.1587\/transfun.2020kep0016","relation":{},"ISSN":["0916-8508","1745-1337"],"issn-type":[{"value":"0916-8508","type":"print"},{"value":"1745-1337","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,1]]},"article-number":"2020KEP0016"}}