While part of the Computer Science department, much of our work occurs at the level of the hardware-software interface. Our current research focus is on how to efficiently provide computing performance in situations where the capabilities of a standard microprocessor do not suffice, or its energy requirements would be excessive.

As an alternative, we propose adaptive computers: combining a smaller, low-power microprocessor with a highly optimized reconfigurable compute unit. The structure of the latter can then be optimally adapted to the precise needs of the current application, and thus provide the required compute power with reduced energy consumption.

To achieve this goal, we realize hardware demonstrators for such computer architectures (including the required operating system ports), and evaluate these using practical applications from a variety of fields. After very promising results, we have now concentrated on making the potential of such computers available even to developers who lack the skills in hardware design that are still required to program such systems. To this end, we have been working on a complete compile flow for partitioning a program in a software high-level programming language for separate execution on the two compute units. The part assigned to the reconfigurable compute unit is then processed further using techniques from hardware synthesis and physical chip design (mapping, placement, routing).

Many of our research efforts rely on support by motivated students with appropriate skills and experience. Thus, our group also develops lectures and labs on the wide range of topics listed above.


  • ESA wins Best Paper Award at DASIP 2021

    Our work DExIE - An IoT-Class Hardware Monitor for Real-Time Fine-Grained Control-Flow Integrity has won a Best Paper Award at DASIP2021. We are extremely happy. Thanks to the committee for selecting our work!

    Congratulations also to the other nominees on their excellent work!

    By Christoph Spang, 2.02.2021

  • ESA wins Best Paper Award at FCCM 2020

    We are extremely happy that our work Comparison of Arithmetic Number Formats for Inference in Sum-Product Networks on FPGAs together with our colleague Martin Kumm has won the Best Paper Award at this year’s FCCM2020. Thanks to the committee for selecting our work!

    Congratulations also to the other nominees on their excellent work!

    Unfortunately, the Corona pandemic did not allow us to present our paper in person, but a video of the talk is available for free at the FCCM virtual conference, where you can also find talks and forums for all other papers accepted at FCCM.

    By Lukas Sommer, 5.05.2020

  • First public release of lectureStudio available!

    We have been developing and using our tool lectureStudio for lecture recordings for many years now. After a phase of intensive beta-testing, we are releasing the first public version today! We hope that the tool will help many people to set up alternative e-learning formats in these challenging times, where the Corona-pandemic does not allow us to come together in person to give lectures.

    lectureStudio does not only provide an easy-to-use interface for lecture recordings, but also allows to augment slides during the lecture with free-hand drawings and highlighting. Next to that, lectureStudio comes with a number of interesting features for interactive lectures, such as messaging or quizzes.

    lectureStudio is free to use and we will open-source its source-code in the near future. You can find installers and documentation on the software page.

    By Lukas Sommer, 23.04.2020

  • Paper accepted for FCCM 2020

    Our work titled Comparison of Arithmetic Number Formats for Inference in Sum-Product Networks on FPGAs has been accepted for publication at FCCM2020. The paper investigates the suitability of three different hardware arithmetic formats for the implementation of FPGA-based hardware accelerators for inference in Sum-Product Networks. This work integrates with our previous work on hardware accelerators presented at TPM2018, ICCD2018, FPT2019 and H2RC2019.

    Next to researchers from ESA, our former colleague and now professor at HS Fulda, Martin Kumm, contributed to this work. We would also like to thank our colleagues Alejandro Molina and Kristian Kersting from the Machine Learning Lab at TU Darmstadt.

    As FCCM will be held as virtual event this year, we will provide a recording of the paper talk in May.

    By Lukas Sommer, 27.03.2020

  • Open PhD Positions

    We are currently actively looking to hire a Ph.D. student (Wissenschaftliche/r Mitarbeiter/in) of any gender to do research on extending RISC-V processors. More information is available here

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    By Prof. Dr-Ing. Andreas Koch, 18.12.2019

  • Open position for a student assistant (all genders)

    The Embedded Systems and Applications Group (ESA) at TU Darmstadt currently has an open position for a student assistant (all genders). ESA is looking for a student who will support the team in a new project by developing Machine Learning (ML) solutions for practically relevant applications as benchmarks for acceleration toolflows on multiple platforms (CPU, GPU, FPGA).

    For more details, have a look at the job offer and contact Lukas Sommer in case of any questions.

    By Lukas Sommer, 17.12.2019

You can find more news in our archive.