{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:49:33Z","timestamp":1767340173554,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031236174"},{"type":"electronic","value":"9783031236181"}],"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\/978-3-031-23618-1_40","type":"book-chapter","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T07:05:49Z","timestamp":1675062349000},"page":"594-605","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Enhancing Energy-Efficiency by\u00a0Solving the\u00a0Throughput Bottleneck of\u00a0LSTM Cells for\u00a0Embedded FPGAs"],"prefix":"10.1007","author":[{"given":"Chao","family":"Qian","sequence":"first","affiliation":[]},{"given":"Tianheng","family":"Ling","sequence":"additional","affiliation":[]},{"given":"Gregor","family":"Schiele","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,31]]},"reference":[{"key":"40_CR1","doi-asserted-by":"crossref","unstructured":"Azari, E., Vrudhula, S.: An energy-efficient reconfigurable LSTM accelerator for natural language processing. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 4450\u20134459. IEEE (2019)","DOI":"10.1109\/BigData47090.2019.9006030"},{"key":"40_CR2","doi-asserted-by":"crossref","unstructured":"Burger, A., Qian, C., Schiele, G., Helms, D.: An embedded CNN implementation for on-device ECG analysis. In: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 1\u20136. IEEE (2020)","DOI":"10.1109\/PerComWorkshops48775.2020.9156260"},{"key":"40_CR3","doi-asserted-by":"crossref","unstructured":"Cao, S., et al.: Efficient and effective sparse LSTM on FPGA with bank-balanced sparsity. In: Proceedings of the 2019 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, pp. 63\u201372 (2019)","DOI":"10.1145\/3289602.3293898"},{"key":"40_CR4","doi-asserted-by":"crossref","unstructured":"Chen, J., Hong, S., He, W., Moon, J., Jun, S.W.: Eciton: very low-power LSTM neural network accelerator for predictive maintenance at the edge. In: 2021 31st International Conference on Field-Programmable Logic and Applications (FPL), pp. 1\u20138. IEEE (2021)","DOI":"10.1109\/FPL53798.2021.00009"},{"key":"40_CR5","doi-asserted-by":"crossref","unstructured":"Fu, R., Zhang, Z., Li, L.: Using LSTM and GRU neural network methods for traffic flow prediction. In: 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 324\u2013328. IEEE (2016)","DOI":"10.1109\/YAC.2016.7804912"},{"key":"40_CR6","unstructured":"Manjunath, N.K., Paneliya, H., Hosseini, M., Hairston, W.D., Mohsenin, T., et al.: A low-power LSTM processor for multi-channel brain EEG artifact detection. In: 2020 21st International Symposium on Quality Electronic Design (ISQED), pp. 105\u2013110. IEEE (2020)"},{"key":"40_CR7","doi-asserted-by":"crossref","unstructured":"Meher, P.K.: An optimized lookup-table for the evaluation of sigmoid function for artificial neural networks. In: 2010 18th IEEE\/IFIP International Conference on VLSI and System-on-Chip, pp. 91\u201395. IEEE (2010)","DOI":"10.1109\/VLSISOC.2010.5642617"},{"key":"40_CR8","doi-asserted-by":"crossref","unstructured":"Namin, A.H., Leboeuf, K., Wu, H., Ahmadi, M.: Artificial neural networks activation function HDL coder. In: 2009 IEEE International Conference on Electro\/Information Technology, pp. 389\u2013392. IEEE (2009)","DOI":"10.1109\/EIT.2009.5189648"},{"key":"40_CR9","unstructured":"Olah, C.: Understanding LSTM Networks [EB\/OL]. https:\/\/colah.github.io\/posts\/2015-08-Understanding-LSTMs\/. Accessed 27 Aug 2015"},{"key":"40_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-11179-7_1","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2014","author":"S Otte","year":"2014","unstructured":"Otte, S., Liwicki, M., Zell, A.: Dynamic cortex memory: enhancing recurrent neural networks for gradient-based sequence learning. In: Wermter, S., et al. (eds.) ICANN 2014. LNCS, vol. 8681, pp. 1\u20138. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-11179-7_1"},{"key":"40_CR11","doi-asserted-by":"crossref","unstructured":"Sun, Z., et al.: FPGA acceleration of LSTM based on data for test flight. In: 2018 IEEE International Conference on Smart Cloud (SmartCloud), pp. 1\u20136. IEEE (2018)","DOI":"10.1109\/SmartCloud.2018.00009"},{"key":"40_CR12","unstructured":"Xilinx: Power Analysis and Optimization, rev. 2, January 2021"},{"key":"40_CR13","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: A power-efficient accelerator based on FPGAs for LSTM network. In: 2017 IEEE International Conference on Cluster Computing (CLUSTER), pp. 629\u2013630. IEEE (2017)","DOI":"10.1109\/CLUSTER.2017.45"}],"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\/978-3-031-23618-1_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T06:48:41Z","timestamp":1728802121000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-23618-1_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031236174","9783031236181"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-23618-1_40","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 January 2023","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":"Grenoble","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2022.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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1060","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":"236","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":"22% - 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":"3-4","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":"17 demo track papers have been accepted from 28 submissions","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)"}}]}}