Lukas Einhaus, M. Sc.

Portrait photo of Lukas Einhaus in front of a grey/blue background

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.

Until March 2022 he worked on the research project “KI-Sprung: LUTNet – An Energy-Efficient AI Network Based on Elementary Lookup Tables,” which was funded by the Federal Ministry of Education and Research. This project investigated how pre-trained and pre-optimized neural networks can be used to develop AI solutions that that run with high efficiency on FPGAs.

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.

M.Sc. Lukas Einhaus

Informatik / AI

Address
Bismarckstr. 90 (BC)
47057 Duisburg
Room
BC 104
Phone
+49 203 379 4399
Fax
+49 203 379 5220
E-Mail
lukas.einhaus@uni-due.de

Functions

  • Wissenschaftliche/r Mitarbeiter/in, Intelligente Eingebettete Systeme

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.

    Journal articles

  • Ling, Tianheng; Qian, Chao; Klann, Theodor Mario; Hoever, Julian; Einhaus, Lukas; Schiele, Gregor
    Configurable Multi-Layer Perceptron-Based Soft Sensors on Embedded Field Programmable Gate Arrays : Targeting Diverse Deployment Goals in Fluid Flow Estimation
    In: Sensors, Vol. 25, 2025, Nr. 1, 83
    DOI (Open Access)
  • Cichiwskyj, Christopher; Schmeißer, Stephan; Qian, Chao; Einhaus, Lukas; Ringhofer, Christopher; Schiele, Gregor
    Elastic AI : System support for adaptive machine learning in pervasive computing systems
    In: CCF Transactions on Pervasive Computing and Interaction, Vol. 3, 2021, Nr. 3, pp. 300 – 328
    DOI (Open Access)

    • Book articles / Proceedings papers

  • Qian, Chao; Einhaus, Lukas; Schiele, Gregor
    ElasticAI-Creator: Optimizing Neural Networks for Time-Series-Analysis for On-Device Machine Learning in IoT Systems
    In: 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.). New York: Association for Computing Machinery (ACM), 2022, pp. 941 – 946
    DOI (Open Access)
  • Kettner, Mark; Perjikolaei, Behnam Razi; Einhaus, Lukas; Ringhofer, Christopher; Qian, Chao; Schiele, Gregor
    FPGA based low latency, low power stream processing AI
    In: 2nd European Workshop on On-Board Data Processing (OBDP2021) / 2nd European Workshop on On-Board Data Processing (OBDP2021), 14-17 June 2021, Online, (Session 3) 2021. Zenodo, 2021
    DOI (Open Access)
  • Einhaus, Lukas; Qian, Chao; Ringhofer, Christopher; Schiele, Gregor
    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. Piscataway: IEEE, 2021, pp. 320 – 321
    DOI
  • Einhaus, Lukas; Qian, Chao; Ringhofer, Christopher; Schiele, Gregor
    Towards Precomputed 1D-Convolutional Layers for Embedded FPGAs
    In: 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.). Cham: Springer, 2021, pp. 327 – 338
    DOI

    • Posters / Proceedings posters

  • Helms, Domenik; Kettner, Mark; Perjikolaei, Behnam Razi; Einhaus, Lukas; Ringhofer, Christopher; Qian, Chao; Schiele, Gregor
    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
    DOI (Open Access)