{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:19:36Z","timestamp":1743121176523,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031150739"},{"type":"electronic","value":"9783031150746"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-15074-6_26","type":"book-chapter","created":{"date-parts":[[2022,8,13]],"date-time":"2022-08-13T12:06:36Z","timestamp":1660392396000},"page":"406-419","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["LSTM Acceleration with\u00a0FPGA and\u00a0GPU Devices for\u00a0Edge Computing Applications in\u00a0B5G MEC"],"prefix":"10.1007","author":[{"given":"Dimitrios","family":"Danopoulos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ioannis","family":"Stamoulias","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Lentaris","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimosthenis","family":"Masouros","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ioannis","family":"Kanaropoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas Kosmas","family":"Kakolyris","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimitrios","family":"Soudris","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"26_CR1","unstructured":"Bai, J., Lu, F., Zhang, K., et al.: Onnx: open neural network exchange (2019). https:\/\/github.com\/onnx\/onnx"},{"key":"26_CR2","unstructured":"Bank, D., Koenigstein, N., Giryes, R.: Autoencoders. CoRR abs\/2003.05991 (2020). https:\/\/arxiv.org\/abs\/2003.05991"},{"key":"26_CR3","unstructured":"Chang, A., Martini, B., Culurciello, E.: Recurrent neural networks hardware implementation on FPGA, November 2015"},{"key":"26_CR4","doi-asserted-by":"publisher","unstructured":"Chang, A.X.M., Culurciello, E.: Hardware accelerators for recurrent neural networks on FPGA. In: 2017 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1\u20134 (2017). https:\/\/doi.org\/10.1109\/ISCAS.2017.8050816","DOI":"10.1109\/ISCAS.2017.8050816"},{"key":"26_CR5","doi-asserted-by":"publisher","unstructured":"Diamanti, A., Vilchez, J.M.S., Secci, S.: LSTM-based radiography for anomaly detection in softwarized infrastructures. In: 2020 32nd International Teletraffic Congress (ITC 32), pp. 28\u201336 (2020). https:\/\/doi.org\/10.1109\/ITC3249928.2020.00012","DOI":"10.1109\/ITC3249928.2020.00012"},{"issue":"8","key":"26_CR6","doi-asserted-by":"publisher","first-page":"3127","DOI":"10.1109\/TNNLS.2019.2935975","volume":"31","author":"T Ergen","year":"2020","unstructured":"Ergen, T., Kozat, S.S.: Unsupervised anomaly detection with LSTM neural networks. IEEE Trans. Neural Netw. Learn. Syst. 31(8), 3127\u20133141 (2020). https:\/\/doi.org\/10.1109\/TNNLS.2019.2935975","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"26_CR7","unstructured":"EU: H2020 project AI@EDGE (2022). https:\/\/aiatedge.eu\/"},{"key":"26_CR8","doi-asserted-by":"publisher","unstructured":"Fowers, J., et al.: A configurable cloud-scale DNN processor for real-time AI. In: 2018 ACM\/IEEE 45th Annual International Symposium on Computer Architecture (ISCA), pp. 1\u201314 (2018). https:\/\/doi.org\/10.1109\/ISCA.2018.00012","DOI":"10.1109\/ISCA.2018.00012"},{"key":"26_CR9","doi-asserted-by":"publisher","unstructured":"Homayouni, H., Ghosh, S., Ray, I., Gondalia, S., Duggan, J., Kahn, M.: An autocorrelation-based LSTM-autoencoder for anomaly detection on time-series data, pp. 5068\u20135077, December 2020. https:\/\/doi.org\/10.1109\/BigData50022.2020.9378192","DOI":"10.1109\/BigData50022.2020.9378192"},{"key":"26_CR10","unstructured":"Nvidia: Jetson AGX Xavier Developer Kit (2022). https:\/\/developer.nvidia.com\/embedded\/jetson-agx-xavier-developer-kit"},{"key":"26_CR11","unstructured":"Nvidia: NVIDIA CUDA-X (2022)"},{"key":"26_CR12","unstructured":"Nvidia: NVIDIA V100 TENSOR CORE GPU (2022). https:\/\/www.nvidia.com\/en-us\/data-center\/v100\/"},{"key":"26_CR13","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"26_CR14","doi-asserted-by":"publisher","unstructured":"Provotar, A., Linder, Y., Veres, M.: Unsupervised anomaly detection in time series using LSTM-based autoencoders, pp. 513\u2013517, December 2019. https:\/\/doi.org\/10.1109\/ATIT49449.2019.9030505","DOI":"10.1109\/ATIT49449.2019.9030505"},{"key":"26_CR15","doi-asserted-by":"publisher","unstructured":"Rybalkin, V., Pappalardo, A., Ghaffar, M., Gambardella, G., Wehn, N., Blott, M.: Finn-l: library extensions and design trade-off analysis for variable precision LSTM networks on FPGAs, pp. 89\u2013897, August 2018. https:\/\/doi.org\/10.1109\/FPL.2018.00024","DOI":"10.1109\/FPL.2018.00024"},{"key":"26_CR16","unstructured":"Xilinx: Alveo U280 Data Center Accelerator Card (2022). https:\/\/www.xilinx.com\/products\/boards-and-kits\/alveo\/u280.html"},{"key":"26_CR17","unstructured":"Xilinx: Vitis (2022). https:\/\/www.xilinx.com\/products\/design-tools\/vitis\/vitis-platform.html"},{"key":"26_CR18","unstructured":"Xilinx: Vitis AI (2022). https:\/\/www.xilinx.com\/developer\/products\/vitis-ai.html"},{"key":"26_CR19","unstructured":"Xilinx: Zynq UltraScale+ MPSoC ZCU104 Evaluation Kit (2022). https:\/\/www.xilinx.com\/products\/boards-and-kits\/zcu104.html"},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: H-LSTM: co-designing highly-efficient large LSTM training via exploiting memory-saving and architectural design opportunities. In: 2021 ACM\/IEEE 48th Annual International Symposium on Computer Architecture (ISCA), pp. 567\u2013580 (2021)","DOI":"10.1109\/ISCA52012.2021.00051"},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Zheng, B., Vijaykumar, N., Pekhimenko, G.: Echo: compiler-based GPU memory footprint reduction for LSTM RNN training. In: 2020 ACM\/IEEE 47th Annual International Symposium on Computer Architecture (ISCA), pp. 1089\u20131102 (2020)","DOI":"10.1109\/ISCA45697.2020.00092"}],"container-title":["Lecture Notes in Computer Science","Embedded Computer Systems: Architectures, Modeling, and Simulation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-15074-6_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T18:07:11Z","timestamp":1666807631000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-15074-6_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031150739","9783031150746"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-15074-6_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"14 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SAMOS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Embedded Computer Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Samos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"3 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 July 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":"samos2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/samos-conference.com\/wp\/","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":"SoftConference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"44","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":"21","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":"48% - 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":"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":"2","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)"}}]}}