Lukas Einhaus, M. Sc.
Short CV
Lukas Einhaus, M.Sc. works since April 2020 as a researcher and PhD student at the Embedded Systems department of the University of Duisburg-Essen. After his Bachelors thesis about programming abstractions for concurrent embedded systems he received his Masters degree with a focus on distributed, reliable systems at the University Duisburg-Essen. In his Masters thesis he did research on quantizing neural networks.
Research
My research focuses on techniques for designing neural networks that allow for an efficient implementation in hardware, especially on FPGAs. My main interest herein lies on so called quantized or low precision neural networks. These differ from conventional full precision networks in the heavily reduced bit depth used for computational operations or the representation of the information flow.
Informatik / AI
47057 Duisburg
Functions
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Wissenschaftliche/r Mitarbeiter/in, Intelligente Eingebettete Systeme
Current lectures
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2024 WS
Past lectures (max. 10)
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2024 SS
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2023 WS
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2023 SS
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2022 WS
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2022 SS
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2021 WS
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2021 SS
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2020 WS
The following publications are listed in the online university bibliography of the University of Duisburg-Essen. Further information may also be found on the person's personal web pages.
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Elastic AI : System support for adaptive machine learning in pervasive computing systemsIn: CCF Transactions on Pervasive Computing and Interaction Vol. 3 (2021) Nr. 3, pp. 300 - 328Online Full Text: dx.doi.org/ (Open Access)
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ElasticAI-Creator: Optimizing Neural Networks for Time-Series-Analysis for On-Device Machine Learning in IoT SystemsIn: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems (SenSys 2022) / 20th ACM Conference on Embedded Networked Sensor Systems (SenSys 2022): 6 - 9 November 2022; Boston, USA / Gummeson, Jeremy; Lee, Sunghoon Ivan (Eds.) 2022, pp. 941 - 946Online Full Text: dx.doi.org/ (Open Access)
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In-Situ Artificial Intelligence for Self-∗ Devices : The Elastic AI Ecosystem (Tutorial)In: 2nd IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2021: Proceedings / 2nd IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2021, Virtual, Washington, 27 September - 1 October 2021 2021, pp. 320 - 321Online Full Text: dx.doi.org/
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Towards Precomputed 1D-Convolutional Layers for Embedded FPGAsIn: Machine Learning and Principles and Practice of Knowledge Discovery in Databases: Proceedings, Part I / International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021 / Kamp, Michael; Koprinska, Irena; Bibal, Adrien; Bouadi, Tassadit; Frénay, Benoît; Galárraga, Luis; Oramas, José.; Adilova, Linara; Krishnamurthy, Yamuna; Kang, Bo; Largeron, Christine; Lijffijt, Jefrey; Viard, Tiphaine; Welke, Pascal; Ruocco, Massimiliano; Aune, Erlend; Gallicchio, Claudio; Schiele, Gregor; Pernkopf, Franz; Blott, Michaela; Fröning, Holger; Schindler, Günther; Guidotti, Riccardo; Monreale, Anna; Rinzivillo, Salvatore; Biecek, Przemyslaw; Ntoutsi, Eirini; Pechenizkiy, Mykola; Rosenhahn, Bodo; Buckley, Christopher; Cialfi, Daniela; Lanillos, Pablo; Ramstead, Maxwell; Verbelen, Tim; Ferreira, Pedro M.; Andresini, Giuseppina; Malerba, Donato; Medeiros, Ibéria; Fournier-Viger, Philippe; Nawaz, M. Saqib; Ventura, Sebastian; Sun, Meng; Zhou, Min; Bitetta, Valerio; Bordino, Ilaria; Ferretti, Andrea; Gullo, Francesco; Ponti, Giovanni; Severini, Lorenzo; Ribeiro, Rita; Gama, João; Gavaldà, Ricard; Cooper, Lee; Ghazaleh, Naghmeh; Richiardi, Jonas; Roqueiro, Damian; Saldana Miranda, Diego; Sechidis, Konstantinos; Graça, Guilherme (Eds.) 2021, pp. 327 - 338Online Full Text: dx.doi.org/
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FPGA based low latency, low power stream processing AI
2nd European Workshop on On-Board Data Processing (OBDP2021), 14-17 June 2021, Online, (Session 3),(2021)Online Full Text: dx.doi.org/ (Open Access)