8 Publications
2023 | Book Chapter | LibreCat-ID: 45899 | 
A. Boschmann, L. Clausing, F. Jentzsch, H. Ghasemzadeh Mohammadi, and M. Platzner, “Flexible Industrial Analytics on Reconfigurable Systems-On-Chip,” in On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412, C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 225–236.
LibreCat
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2023 | Conference Paper | LibreCat-ID: 45913
L. Clausing, Z. Guetattfi, P. Kaufmann, C. Lienen, and M. Platzner, “On Guaranteeing Schedulability of Periodic Real-time Hardware Tasks under ReconOS64,” 2023.
LibreCat
2023 | Book | LibreCat-ID: 45863 |
C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023.
LibreCat
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2022 | Conference Paper | LibreCat-ID: 32855
L. Clausing and M. Platzner, “ReconOS64: A Hardware Operating System for Modern Platform FPGAs with 64-Bit Support,” in 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Lyon, France, 2022, pp. 120–127, doi: 10.1109/ipdpsw55747.2022.00029.
LibreCat
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2021 | Conference Paper | LibreCat-ID: 30909
L. Clausing, “ReconOS64: High-Performance Embedded Computing for Industrial Analytics on a Reconfigurable System-on-Chip,” 2021, doi: 10.1145/3468044.3468056.
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2021 | Conference Paper | LibreCat-ID: 30908
H. Ghasemzadeh Mohammadi et al., “FLight: FPGA Acceleration of Lightweight DNN Model Inference in Industrial Analytics,” 2021, doi: https://doi.org/10.1007/978-3-030-93736-2_27.
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2020 | Journal Article | LibreCat-ID: 13770
H. Karl, D. Kundisch, F. Meyer auf der Heide, and H. Wehrheim, “A Case for a New IT Ecosystem: On-The-Fly Computing,” Business & Information Systems Engineering, vol. 62, no. 6, pp. 467–481, 2020, doi: 10.1007/s12599-019-00627-x.
LibreCat
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2020 | Conference Paper | LibreCat-ID: 20808
H. Ghasemzadeh Mohammadi et al., “DeepWind: An Accurate Wind Turbine Condition Monitoring Framework via Deep Learning on Embedded Platforms,” 2020, doi: 10.1109/etfa46521.2020.9211880.
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