{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:27:50Z","timestamp":1743128870930,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030937355"},{"type":"electronic","value":"9783030937362"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-93736-2_27","type":"book-chapter","created":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T21:02:28Z","timestamp":1645131748000},"page":"351-362","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FLight: FPGA Acceleration of Lightweight DNN Model Inference in Industrial Analytics"],"prefix":"10.1007","author":[{"given":"Hassan Ghasemzadeh","family":"Mohammadi","sequence":"first","affiliation":[]},{"given":"Felix Paul","family":"Jentzsch","sequence":"additional","affiliation":[]},{"given":"Maurice","family":"Kuschel","sequence":"additional","affiliation":[]},{"given":"Rahil","family":"Arshad","sequence":"additional","affiliation":[]},{"given":"Sneha","family":"Rautmare","sequence":"additional","affiliation":[]},{"given":"Suraj","family":"Manjunatha","sequence":"additional","affiliation":[]},{"given":"Marco","family":"Platzner","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Boschmann","sequence":"additional","affiliation":[]},{"given":"Dirk","family":"Schollbach","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,17]]},"reference":[{"key":"27_CR1","unstructured":"BLADEcontrol condition monitoring system. Weidm\u00fcller Monitoring Systems GmbH. https:\/\/mdcop.weidmueller.com\/mediadelivery\/asset\/900_87890"},{"key":"27_CR2","unstructured":"Keras: The Python Deep Learning API. https:\/\/keras.io"},{"key":"27_CR3","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)"},{"key":"27_CR4","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.renene.2012.04.020","volume":"48","author":"A Kusiak","year":"2012","unstructured":"Kusiak, A., Verma, A.: Analyzing bearing faults in wind turbines: a data-mining approach. Renewable Energy 48, 110\u2013116 (2012)","journal-title":"Renewable Energy"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Lu, D., Qiao, W.: Frequency demodulation-aided condition monitoring for drivetrain gearboxes. In: 2013 IEEE Transportation Electrification Conference and Expo (ITEC), pp. 1\u20136. IEEE (2013)","DOI":"10.1109\/ITEC.2013.6574526"},{"key":"27_CR6","doi-asserted-by":"crossref","unstructured":"Mohammadi, H.G., et al.: DeepWind: an accurate wind turbine condition monitoring framework via deep learning on embedded platforms. In: 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), vol. 1, pp. 1431\u20131434. IEEE (2020)","DOI":"10.1109\/ETFA46521.2020.9211880"},{"key":"27_CR7","doi-asserted-by":"publisher","first-page":"102990","DOI":"10.1016\/j.micpro.2020.102990","volume":"73","author":"PG Mousouliotis","year":"2020","unstructured":"Mousouliotis, P.G., Petrou, L.P.: CNN-grinder: from algorithmic to high-level synthesis descriptions of CNNs for low-end-low-cost FPGA SoCs. Microprocess. Microsyst. 73, 102990 (2020)","journal-title":"Microprocess. Microsyst."},{"key":"27_CR8","unstructured":"Oyague, F., Butterfield, C., Sheng, S.: NREL gearbox reliability collaborative analysis round robin. Technical report, National Renewable Energy Lab. (NREL), Golden, CO, United States (2009)"},{"key":"27_CR9","doi-asserted-by":"crossref","unstructured":"Qiu, J., et al.: Going deeper with embedded FPGA platform for convolutional neural network. In: Proceedings of the 2016 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, pp. 26\u201335 (2016)","DOI":"10.1145\/2847263.2847265"},{"issue":"1","key":"27_CR10","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.asoc.2012.08.033","volume":"13","author":"M Schlechtingen","year":"2013","unstructured":"Schlechtingen, M., Santos, I.F., Achiche, S.: Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: system description. Appl. Soft Comput. 13(1), 259\u2013270 (2013)","journal-title":"Appl. Soft Comput."},{"key":"27_CR11","unstructured":"Steinarsson, S.: Downsampling time series for visual representation. Ph.D. thesis (2013)"},{"issue":"3","key":"27_CR12","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1109\/MCSE.2010.69","volume":"12","author":"JE Stone","year":"2010","unstructured":"Stone, J.E., Gohara, D., Shi, G.: OpenCL: a parallel programming standard for heterogeneous computing systems. Comput. Sci. Eng. 12(3), 66 (2010)","journal-title":"Comput. Sci. Eng."},{"issue":"2","key":"27_CR13","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1109\/TNNLS.2018.2844093","volume":"30","author":"SI Venieris","year":"2018","unstructured":"Venieris, S.I., Bouganis, C.S.: fpgaConvNet: mapping regular and irregular convolutional neural networks on FPGAs. IEEE Trans. Neural Netw. Learn. Syst. 30(2), 326\u2013342 (2018)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"27_CR14","unstructured":"Wirbel, L.: Xilinx SDAccel: a unified development environment for tomorrow\u2019s data center. The Linley Group Inc, p. 24 (2014)"},{"issue":"10","key":"27_CR15","doi-asserted-by":"publisher","first-page":"2668","DOI":"10.1109\/TCAD.2019.2930577","volume":"39","author":"Y Xing","year":"2019","unstructured":"Xing, Y., et al.: DNNVM: end-to-end compiler leveraging heterogeneous optimizations on FPGA-based CNN accelerators. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 39(10), 2668\u20132681 (2019)","journal-title":"IEEE Trans. Comput. Aided Des. Integr. Circuits Syst."},{"issue":"11","key":"27_CR16","doi-asserted-by":"publisher","first-page":"2072","DOI":"10.1109\/TCAD.2017.2785257","volume":"38","author":"C Zhang","year":"2018","unstructured":"Zhang, C., Sun, G., Fang, Z., Zhou, P., Pan, P., Cong, J.: Caffeine: toward uniformed representation and acceleration for deep convolutional neural networks. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 38(11), 2072\u20132085 (2018)","journal-title":"IEEE Trans. Comput. Aided Des. Integr. Circuits Syst."},{"key":"27_CR17","doi-asserted-by":"publisher","first-page":"83224","DOI":"10.1109\/ACCESS.2020.2988311","volume":"8","author":"J Zhu","year":"2020","unstructured":"Zhu, J., Wang, L., Liu, H., Tian, S., Deng, Q., Li, J.: An efficient task assignment framework to accelerate DPU-based convolutional neural network inference on FPGAs. IEEE Access 8, 83224\u201383237 (2020)","journal-title":"IEEE Access"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-93736-2_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T02:14:11Z","timestamp":1651803251000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-93736-2_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030937355","9783030937362"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-93736-2_27","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"17 February 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bilbao","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2021.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"869","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":"210","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":"0","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":"24% - 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":"3-4","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":"3-9","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held online due to the COVID-19 pandemic.","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)"}}]}}