{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:52:03Z","timestamp":1743018723335,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031341069"},{"type":"electronic","value":"9783031341076"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-34107-6_42","type":"book-chapter","created":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T09:04:12Z","timestamp":1685523852000},"page":"529-540","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Lossless Method of\u00a0Constraining Membrane Potential in\u00a0Deep Spiking Neural Networks"],"prefix":"10.1007","author":[{"given":"Yijie","family":"Miao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Makoto","family":"Ikeda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,1]]},"reference":[{"key":"42_CR1","doi-asserted-by":"publisher","unstructured":"Javanshir, A., Nguyen, T.T., Mahmud, M.A.P., Kouzani, A.Z.: Advancements in algorithms and neuromorphic hardware for spiking neural networks. Neural Comput. 34, 1289\u20131328 (2022). https:\/\/doi.org\/10.1162\/neco_a_01499","DOI":"10.1162\/neco_a_01499"},{"issue":"1","key":"42_CR2","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1007\/s11263-014-0788-3","volume":"113","author":"Y Cao","year":"2014","unstructured":"Cao, Y., Chen, Y., Khosla, D.: Spiking deep convolutional neural networks for energy-efficient object recognition. Int. J. Comput. Vision 113(1), 54\u201366 (2014). https:\/\/doi.org\/10.1007\/s11263-014-0788-3","journal-title":"Int. J. Comput. Vision"},{"key":"42_CR3","doi-asserted-by":"publisher","unstructured":"Rueckauer, B., Lungu, I.-A., Hu, Y., Pfeiffer, M., Liu, S.-C.: Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification. Front. Neurosci. 11, (2017). https:\/\/doi.org\/10.3389\/fnins.2017.00682","DOI":"10.3389\/fnins.2017.00682"},{"key":"42_CR4","doi-asserted-by":"publisher","unstructured":"Hu, Y., Tang, H., Pan, G.: Spiking Deep Residual Networks. IEEE Trans. Neural Netw. Learn. Syst. 1\u20136, Early Access (2021). https:\/\/doi.org\/10.1109\/TNNLS.2021.3119238","DOI":"10.1109\/TNNLS.2021.3119238"},{"key":"42_CR5","doi-asserted-by":"publisher","unstructured":"Hwang, S., et al.: Low-Latency spiking neural networks using pre-charged membrane potential and delayed evaluation. Front. Neurosci. 15 (2021). https:\/\/doi.org\/10.3389\/fnins.2021.629000","DOI":"10.3389\/fnins.2021.629000"},{"key":"42_CR6","unstructured":"Wang, Z., Lian, S., Zhang, Y., Cui, X., Yan, R., Tang, H.: Towards lossless ANNSNN conversion under ultra-low latency with dual-phase optimization, arXiv preprint arXiv:2205.07473 (2022)"},{"key":"42_CR7","doi-asserted-by":"publisher","unstructured":"Kang, Z., Wang, L., Guo, S., Gong, R., Deng, Y., Dou, Q.: ASIE: an asynchronous SNN inference engine for AER events processing. In: 2019 25th IEEE International Symposium on Asynchronous Circuits and Systems (ASYNC), pp. 48\u201357 (2019). https:\/\/doi.org\/10.1109\/ASYNC.2019.00015","DOI":"10.1109\/ASYNC.2019.00015"},{"key":"42_CR8","doi-asserted-by":"publisher","unstructured":"Zhang, J., Wu, H., Wei, J., Wei, S., Chen, H.: An asynchronous reconfigurable SNN accelerator with event-driven time step update. In: 2019 IEEE Asian Solid-State Circuits Conference (A-SSCC), pp. 213\u2013216 (2019). https:\/\/doi.org\/10.1109\/A-SSCC47793.2019.9056903","DOI":"10.1109\/A-SSCC47793.2019.9056903"},{"key":"42_CR9","doi-asserted-by":"publisher","unstructured":"Ju, X., Fang, B., Yan, R., Xu, X., Tang, H.: An FPGA implementation of deep spiking neural networks for low-power and fast classification. Neural Comput. 32, 182\u2013204 (2020). https:\/\/doi.org\/10.1162\/neco_a_01245","DOI":"10.1162\/neco_a_01245"},{"issue":"2","key":"42_CR10","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1007\/s11390-020-9686-z","volume":"35","author":"S-Q Wang","year":"2020","unstructured":"Wang, S.-Q., Wang, L., Deng, Yu., Yang, Z.-J., Guo, S.-S., Kang, Z.-Y., Guo, Y.-F., Xu, W.-X.: SIES: a novel implementation of spiking convolutional neural network inference engine on field-programmable Gate Array. J. Comput. Sci. Technol. 35(2), 475\u2013489 (2020). https:\/\/doi.org\/10.1007\/s11390-020-9686-z","journal-title":"J. Comput. Sci. Technol."},{"key":"42_CR11","doi-asserted-by":"publisher","unstructured":"Zhang, L., et al.: A cost-efficient high-speed VLSI architecture for spiking convolutional neural network inference using time-step binary spike maps. Sensors (Basel). 21, 6006 (2021). https:\/\/doi.org\/10.3390\/s21186006","DOI":"10.3390\/s21186006"},{"key":"42_CR12","doi-asserted-by":"publisher","unstructured":"Aung, M.T.L., Qu, C., Yang, L., Luo, T., Goh, R.S.M., Wong, W.-F.: DeepFire: acceleration of convolutional spiking neural network on modern field programmable gate arrays. In: 2021 31st International Conference on Field-Programmable Logic and Applications (FPL), pp. 28\u201332. IEEE, Dresden, Germany (2021). https:\/\/doi.org\/10.1109\/FPL53798.2021.00013","DOI":"10.1109\/FPL53798.2021.00013"},{"key":"42_CR13","doi-asserted-by":"crossref","unstructured":"Nallathambi, A., Chandrachoodan, N.: Probabilistic spike propagation for FPGA implementation of spiking neural networks, arXiv preprint arXiv:2001.09725 (2020)","DOI":"10.36227\/techrxiv.11565627.v1"},{"key":"42_CR14","doi-asserted-by":"publisher","first-page":"3515","DOI":"10.1038\/s41598-020-60572-8","volume":"10","author":"S Hwang","year":"2020","unstructured":"Hwang, S., Chang, J., Oh, M.-H., Lee, J.-H., Park, B.-G.: Impact of the sub-resting membrane potential on accurate inference in spiking neural networks. Sci. Rep. 10, 3515 (2020). https:\/\/doi.org\/10.1038\/s41598-020-60572-8","journal-title":"Sci. Rep."},{"key":"42_CR15","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778. IEEE, Las Vegas, NV, USA (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["IFIP Advances in Information and Communication Technology","Artificial Intelligence Applications and Innovations"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-34107-6_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T09:14:32Z","timestamp":1685524472000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34107-6_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031341069","9783031341076"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34107-6_42","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"type":"print","value":"1868-4238"},{"type":"electronic","value":"1868-422X"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Artificial Intelligence Applications and Innovations","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Le\u00f3n","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ifipaiai.org\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"185","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"75","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,8","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,1","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This event was held in hybrid mode.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}