{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T18:20:56Z","timestamp":1763749256157,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":65,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T00:00:00Z","timestamp":1716940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSFCKeyProgram","award":["61932017"],"award-info":[{"award-number":["61932017"]}]},{"name":"UGC\/GRF","award":["15204820","15215421"],"award-info":[{"award-number":["15204820","15215421"]}]},{"DOI":"10.13039\/501100010428","name":"Innovation and Technology Fund","doi-asserted-by":"publisher","award":["ITS\/099\/21"],"award-info":[{"award-number":["ITS\/099\/21"]}],"id":[{"id":"10.13039\/501100010428","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,29]]},"DOI":"10.1145\/3636534.3649384","type":"proceedings-article","created":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T13:32:55Z","timestamp":1716989575000},"page":"603-617","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Binary Optical Machine Learning: Million-Scale Physical Neural Networks with Nano Neurons"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2972-5382","authenticated-orcid":false,"given":"Xueyuan","family":"Yang","sequence":"first","affiliation":[{"name":"The Hong Kong Polytechnic University, Kowloon, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4120-773X","authenticated-orcid":false,"given":"Zhenlin","family":"An","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Kowloon, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3902-3878","authenticated-orcid":false,"given":"Qingrui","family":"Pan","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Kowloon, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9651-0811","authenticated-orcid":false,"given":"Lei","family":"Yang","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Kowloon, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8963-0193","authenticated-orcid":false,"given":"Dangyuan","family":"Lei","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Kowloon, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8610-2072","authenticated-orcid":false,"given":"Yulong","family":"Fan","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Kowloon, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,5,29]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He K.","year":"2016","unstructured":"K. He, X. Zhang, S. Ren, and J. Sun, \"Deep residual learning for image recognition,\" in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770--778.","journal-title":"Proceedings of the IEEE conference on computer vision and pattern recognition"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178115"},{"key":"e_1_3_2_1_3_1","first-page":"162","volume-title":"IEEE","author":"Singh S. P.","year":"2017","unstructured":"S. P. Singh, A. Kumar, H. Darbari, L. Singh, A. Rastogi, and S. Jain, \"Machine translation using deep learning: An overview,\" in 2017 international conference on computer, communications and electronics (comptelix). IEEE, 2017, pp. 162--167."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/mti2030047"},{"key":"e_1_3_2_1_5_1","unstructured":"\"Energy consumption of AI poses environmental problems \" https:\/\/www.techtarget.com\/searchenterpriseai\/feature\/Energy-consumption-of-AI-poses-environmental-problems."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aat8084"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41566-020-00754-y"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-30619-y"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-2038-x"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-2973-6"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevX.9.021032"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-37952-2"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1364\/OPTICA.6.001132"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1364\/OE.27.009620"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.89.235419"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/srep32945"},{"key":"e_1_3_2_1_17_1","unstructured":"\"Ebl price \" https:https:\/\/lab.kni.caltech.edu\/Usage_Rates."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107281"},{"key":"e_1_3_2_1_19_1","volume-title":"A comprehensive review of binary neural network,\" arXiv preprint arXiv:2110.06804","author":"Yuan C.","year":"2021","unstructured":"C. Yuan and S. S. Agaian, \"A comprehensive review of binary neural network,\" arXiv preprint arXiv:2110.06804, 2021."},{"key":"e_1_3_2_1_20_1","volume-title":"Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1,\" arXiv preprint arXiv:1602.02830","author":"Courbariaux M.","year":"2016","unstructured":"M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, and Y. Bengio, \"Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1,\" arXiv preprint arXiv:1602.02830, 2016."},{"key":"e_1_3_2_1_21_1","volume-title":"Bipointnet: Binary neural network for point clouds,\" arXiv preprint arXiv:2010.05501","author":"Qin H.","year":"2020","unstructured":"H. Qin, Z. Cai, M. Zhang, Y. Ding, H. Zhao, S. Yi, X. Liu, and H. Su, \"Bipointnet: Binary neural network for point clouds,\" arXiv preprint arXiv:2010.05501, 2020."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.06.084"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1080\/00031305.1954.10482763"},{"key":"e_1_3_2_1_24_1","volume-title":"Ra-bnn: Constructing robust & accurate binary neural network to simultaneously defend adversarial bit-flip attack and improve accuracy,\" arXiv preprint arXiv:2103.13813","author":"Rakin A. S.","year":"2021","unstructured":"A. S. Rakin, L. Yang, J. Li, F. Yao, C. Chakrabarti, Y. Cao, J.-s. Seo, and D. Fan, \"Ra-bnn: Constructing robust & accurate binary neural network to simultaneously defend adversarial bit-flip attack and improve accuracy,\" arXiv preprint arXiv:2103.13813, 2021."},{"key":"e_1_3_2_1_25_1","volume-title":"How does selective mechanism improve self-attention networks?\" arXiv preprint arXiv:2005.00979","author":"Geng X.","year":"2020","unstructured":"X. Geng, L. Wang, X. Wang, B. Qin, T. Liu, and Z. Tu, \"How does selective mechanism improve self-attention networks?\" arXiv preprint arXiv:2005.00979, 2020."},{"issue":"5","key":"e_1_3_2_1_26_1","first-page":"1095","article-title":"Introduction to fourier optics","volume":"8","author":"Goodman J. W.","year":"1996","unstructured":"J. W. Goodman and P. Sutton, \"Introduction to fourier optics,\" Quantum and Semiclassical Optics-Journal of the European Optical Society Part B, vol. 8, no. 5, p. 1095, 1996.","journal-title":"Quantum and Semiclassical Optics-Journal of the European Optical Society Part B"},{"key":"e_1_3_2_1_27_1","unstructured":"\"Rectangular function \" https:\/\/en.wikipedia.org\/wiki\/Rectangular_function."},{"key":"e_1_3_2_1_28_1","volume-title":"Introduction to Fourier optics","author":"Goodman J. W.","year":"2005","unstructured":"J. W. Goodman, Introduction to Fourier optics. Roberts and Company publishers, 2005."},{"key":"e_1_3_2_1_29_1","unstructured":"\"Cr absorb \" https:\/\/pubs.rsc.org\/en\/content\/articlehtml\/2019\/ra\/c9ra00559e."},{"key":"e_1_3_2_1_30_1","unstructured":"\"Cmos \" https:\/\/www.thorlabs.com\/thorproduct.cfm?partnumber=CS165CU."},{"key":"e_1_3_2_1_31_1","volume-title":"Mnist handwritten digit database,\" ATT Labs [Online]. Available: http:\/\/yann.lecun.com\/exdb\/mnist","author":"LeCun Y.","year":"2010","unstructured":"Y. LeCun, C. Cortes, and C. Burges, \"Mnist handwritten digit database,\" ATT Labs [Online]. Available: http:\/\/yann.lecun.com\/exdb\/mnist, vol. 2, 2010."},{"key":"e_1_3_2_1_32_1","volume-title":"Assessing four neural networks on handwritten digit recognition dataset (mnist),\" arXiv preprint arXiv:1811.08278","author":"Chen F.","year":"2018","unstructured":"F. Chen, N. Chen, H. Mao, and H. Hu, \"Assessing four neural networks on handwritten digit recognition dataset (mnist),\" arXiv preprint arXiv:1811.08278, 2018."},{"key":"e_1_3_2_1_33_1","volume-title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,\" arXiv preprint arXiv:1708.07747","author":"Xiao H.","year":"2017","unstructured":"H. Xiao, K. Rasul, and R. Vollgraf, \"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,\" arXiv preprint arXiv:1708.07747, 2017."},{"key":"e_1_3_2_1_34_1","volume-title":"Enhancing small object encoding in deep neural networks: Introducing fast&focused-net with volume-wise dot product layer,\" arXiv preprint arXiv:2401.09823","author":"Tofik A.","year":"2024","unstructured":"A. Tofik and R. P. Pratim, \"Enhancing small object encoding in deep neural networks: Introducing fast&focused-net with volume-wise dot product layer,\" arXiv preprint arXiv:2401.09823, 2024."},{"key":"e_1_3_2_1_35_1","unstructured":"\"English alphabets \" https:\/\/www.kaggle.com\/datasets\/mohneesh7\/english-alphabets 2017."},{"key":"e_1_3_2_1_36_1","unstructured":"\"Best englishalphabet \" https:\/\/www.kaggle.com\/code\/mohneesh7\/character-recognition#Exploratory-Data-Analysis."},{"key":"e_1_3_2_1_37_1","first-page":"1","article-title":"Gcdb: a character database system","author":"Margaronis J.","year":"2009","unstructured":"J. Margaronis, M. Christou, E. Kavallieratou, and T. Tzouramanis, \"Gcdb: a character database system,\" in Proceedings of the International Workshop on Multilingual OCR, 2009, pp. 1--7.","journal-title":"Proceedings of the International Workshop on Multilingual OCR"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.12988\/ijco.2022.9829"},{"key":"e_1_3_2_1_39_1","first-page":"681","volume-title":"IEEE","author":"Huang S.","year":"2019","unstructured":"S. Huang, H. Wang, Y. Liu, X. Shi, and L. Jin, \"Obc306: A large-scale oracle bone character recognition dataset,\" in 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2019, pp. 681--688."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","unstructured":"A. Kumar and E. Deni Raj \"Silhouettes for human posture recognition \" 2020. [Online]. 10.21227\/9c9b-3j44","DOI":"10.21227\/9c9b-3j44"},{"key":"e_1_3_2_1_41_1","first-page":"783","volume-title":"IEEE","author":"Zhang H.","year":"2020","unstructured":"H. Zhang, S. Shui, Y. Wu, Q. Yang, and Y. Cai, \"Posture recognition based on the improved optical diffractive neural network,\" in 2020 IEEE 3rd International Conference on Electronic Information and Communication Technology (ICEICT). IEEE, 2020, pp. 783--785."},{"key":"e_1_3_2_1_42_1","unstructured":"A. D. I. Pytorch \"Pytorch \" 2018."},{"key":"e_1_3_2_1_43_1","unstructured":"\"Be02-05-b - optical beam expander \" https:\/\/www.thorlabs.com\/thorproduct.cfm?partnumber=BE02-05-B."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1364\/PRJ.389553"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1364\/PRJ.415964"},{"key":"e_1_3_2_1_46_1","unstructured":"\"Quartz transmittance \" https:\/\/www.quora.com\/What-is-the-optical-transmission-of-quartz-Will-it-let-ultraviolet-visible-and-infrared-light-pass-through-How-much-percent-of-sunlight-pass-through-it."},{"key":"e_1_3_2_1_47_1","unstructured":"\"Slm transmittance \" https:\/\/www.lasercomponents.com\/us\/product\/pluto-2-phase-lcos-slm\/."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.09.046"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3297858.3304011"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcis.2020.09.050"},{"key":"e_1_3_2_1_51_1","first-page":"437","volume-title":"Tricks of the Trade","author":"Bengio Y.","year":"2012","unstructured":"Y. Bengio, \"Practical recommendations for gradient-based training of deep architectures,\" in Neural Networks: Tricks of the Trade: Second Edition. Springer, 2012, pp. 437--478."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41377-022-00844-2"},{"key":"e_1_3_2_1_53_1","first-page":"379","volume-title":"Adabin: Improving binary neural networks with adaptive binary sets,\" in European conference on computer vision","author":"Tu Z.","year":"2022","unstructured":"Z. Tu, X. Chen, P. Ren, and Y. Wang, \"Adabin: Improving binary neural networks with adaptive binary sets,\" in European conference on computer vision. Springer, 2022, pp. 379--395."},{"key":"e_1_3_2_1_54_1","first-page":"46","volume-title":"IEEE","author":"Kanade T.","year":"2000","unstructured":"T. Kanade, J. F. Cohn, and Y. Tian, \"Comprehensive database for facial expression analysis,\" in Proceedings fourth IEEE international conference on automatic face and gesture recognition (cat. No. PR00580). IEEE, 2000, pp. 46--53."},{"key":"e_1_3_2_1_55_1","first-page":"94","article-title":"The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression,\" in 2010 ieee computer society conference on computer vision and pattern recognition-workshops","author":"Lucey P.","year":"2010","unstructured":"P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews, \"The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression,\" in 2010 ieee computer society conference on computer vision and pattern recognition-workshops. IEEE, 2010, pp. 94--101.","journal-title":"IEEE"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.123.023901"},{"key":"e_1_3_2_1_57_1","first-page":"3","volume-title":"Optical Society of America","author":"Goi E.","year":"2019","unstructured":"E. Goi and M. Gu, \"Laser printing of a nano-imager to perform full optical machine learning,\" in The European Conference on Lasers and Electro-Optics. Optical Society of America, 2019, p. jsi_p_3."},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2020.07.032"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3476988"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-20719-7"},{"key":"e_1_3_2_1_61_1","volume-title":"Binarized neural networks,\" Advances in neural information processing systems","author":"Hubara I.","year":"2016","unstructured":"I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio, \"Binarized neural networks,\" Advances in neural information processing systems, vol. 29, 2016."},{"key":"e_1_3_2_1_62_1","volume-title":"Training deep neural networks with binary weights during propagations,\" Advances in neural information processing systems","author":"Courbariaux M.","year":"2015","unstructured":"M. Courbariaux, Y. Bengio, and J.-P. David, \"Binaryconnect: Training deep neural networks with binary weights during propagations,\" Advances in neural information processing systems, vol. 28, 2015."},{"key":"e_1_3_2_1_63_1","first-page":"525","volume-title":"Xnor-net: Imagenet classification using binary convolutional neural networks,\" in European conference on computer vision","author":"Rastegari M.","year":"2016","unstructured":"M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi, \"Xnor-net: Imagenet classification using binary convolutional neural networks,\" in European conference on computer vision. Springer, 2016, pp. 525--542."},{"key":"e_1_3_2_1_64_1","volume-title":"Loss-aware binarization of deep networks,\" arXiv preprint arXiv:1611.01600","author":"Hou L.","year":"2016","unstructured":"L. Hou, Q. Yao, and J. T. Kwok, \"Loss-aware binarization of deep networks,\" arXiv preprint arXiv:1611.01600, 2016."},{"key":"e_1_3_2_1_65_1","first-page":"722","volume-title":"Enhancing the performance of 1-bit cnns with improved representational capability and advanced training algorithm,\" in Proceedings of the European conference on computer vision (ECCV)","author":"Liu Z.","year":"2018","unstructured":"Z. Liu, B. Wu, W. Luo, X. Yang, W. Liu, and K.-T. Cheng, \"Bi-real net: Enhancing the performance of 1-bit cnns with improved representational capability and advanced training algorithm,\" in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 722--737."}],"event":{"name":"ACM MobiCom '24: 30th Annual International Conference on Mobile Computing and Networking","sponsor":["SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing"],"location":"Washington D.C. DC USA","acronym":"ACM MobiCom '24"},"container-title":["Proceedings of the 30th Annual International Conference on Mobile Computing and Networking"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3636534.3649384","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3636534.3649384","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:54:12Z","timestamp":1750287252000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3636534.3649384"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,29]]},"references-count":65,"alternative-id":["10.1145\/3636534.3649384","10.1145\/3636534"],"URL":"https:\/\/doi.org\/10.1145\/3636534.3649384","relation":{},"subject":[],"published":{"date-parts":[[2024,5,29]]},"assertion":[{"value":"2024-05-29","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}