Prof. Dr. Jürgen Teich

Department of Computer Science

Our research centers around the systematic design (CAD) of hardware/software systems, ranging from embedded systems to HPC platforms. One principal research direction is domain-specific computing that tries to tackle the very complex programming and design challenge of parallel heterogeneous computer architectures. Domain-specific computing drastically separates the concerns of algorithm development and target architecture implementation, including parallelization and low-level implementation details. The key idea is to take advantage of the knowledge being inherent in a particular problem area or field of application, i.e., a particular domain, in a well-directed manner and thus, to master the complexity of heterogeneous systems. Such domain knowledge can be captured by reasonable abstractions, augmentations, and notations, e.g., libraries, Domain-specific programming languages (DSLs), or combinations of both (e.g., embedded DSLs implemented via template metaprogramming). On this basis, patterns can be utilized to transform and optimize the input description in a goal-oriented way during compilation, and, finally, to generate code for a specific target architecture. Thus, DSLs provide high productivity plus typically also high performance. We develop DSLs and target platform languages to capture both domain and architecture knowledge, which is utilized during the different phases of compilation, parallelization, mapping, as well as code generation for a wide variety of architectures, e.g., multi-core processors, GPUs, MPSoCs, FPGAs. All these steps usually go along with optimizing and exploring the vast space of design options and trading off multiple objectives, such as performance, cost, energy, or reliability.

Research projects

  • Diffusion-weighted imaging and quantitative susceptibility mapping of the breast, liver, prostate, and brain
  • Development of new MRI pulse sequences
  • Development of new MRI post-processing schemes
  • Joint evaluation of new MR methods with radiology
  • Domain-specific Computing for Medical imaging
  • Hipacc – the Heterogeneous Image Processing Acceleration Framework
  • AI Laboratory for System-level Design of ML-based Signal Processing Applications
  • Architecture Modeling and Exploration of Algorithms for Medical Image Processing

Current projects

  • ACoF -- Approximate Computing on FPGAs

    (Third Party Funds Single)

    Term: since 1. June 2021
    Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
    Approximate Computing systematically exploits the trade-off between accuracy, power/energy consumption, performance, and cost of many applications of daily life, e.g., computer vision, machine learning, multimedia, big data analysis and gaming. Computing results approximately is a viable approach here thanks to inherent human perceptual limitations, redundancy, or noise in input data.In this project, we want to investigate novel techniques for the design and optimization of approximate logic circuits for FPGA (field-programmable gate array) targets. These devices are known to perfectly combine high performance of hardware designs with the re-programmability of software and are used in many products of daily life and even cloud servers. The goal of our research is a) to investigate novel techniques for function approximation exploiting FPGA artifacts, i.e., DPS blocks and BRAM, b) to study new error metrics and a calculus for error propagation in networks of approximate arithmetic modules, c) to develop novel FPGA-specific optimization techniques for design space exploration and synthesis of approximate multi-output Boolean functions, and d) study how to integrate error modeling and analysis techniques into existing high-level programming languages and subsequent synthesis of approximate Verilog or VHDL designs.
  • Analysis, optimization, and automatic synthesis of sensor data processing applications on AMMOD base stations

    (Third Party Funds Single)

    Term: 1. November 2019 - 31. October 2022
    Funding source: Bundesministerium für Bildung und Forschung (BMBF)
    URL: https://www.zfmk.de/de/forschung/projekte/ammod-eine-wetterstation-fuer-artenvielfalt

    This project is funded by the Federal Ministry of Education and Research (BMBF) as part of the AMMOD project. The implementation of the project is overseen by the German Aerospace Center (DLR).

    Our planet loses biodiversity year after year. Since 1990, the number of insects and birds in Central Europe has fallen sharply, which has been confirmed by individual studies. However, there is no comprehensive collection and scientific evaluation of such data, as is the case, for example, in climate research. The main reason being that technical prerequisites and infrastructures are lacking.

    The AMMOD project combines innovative technologies and adapts them to automatize the detection of species, in analogy to continuous measurements achieved with autonomous weather stations. For this purpose, the following modules are developed:

    • Automatized sampling of insects, pollen and airborne spores für the identification via DNA barcodes (see GBOL project)
    • Automatized image recognition for birds (among others with a “sky scanner”), mammals, nocturnal insects (“moth scanner”) 
    • Automatized bioacoustics species identification (e.g. for birds, bats, grasshoppers)
    • Automatized analysis of biogenic scents (“smellscape analysis”).

    For the automated monitoring of the occurrence of different species in extensive, often inaccessible areas, these AMMOD technologies as well as the storage, processing and transmission of their data must be integrated on a generic platform, which serves as a "weather station of biodiversity". This platform must be equipped with various sensors and actuators and configurable with various software and hardware components. The challenge in designing this platform is that, on the one hand, such stations have to process, store, and transmit large amounts of data via mobile radio. On the other hand, the available resources in the field are limited (energy based on solar or wind power for a self-sufficient power supply, data storage capacity and communication bandwidth).

    Our subproject deals with sensor data processing and storage within the base station. The subproject has two main objectives. On the one hand, designing a hardware architecture for the AMMOD base station which enables the processing and storage of sensor data energy-efficiently and in real time, but which can be generically adapted for the different application domains. On the other hand, the partners of other subprojects will be supported in the implementation of their algorithms in software and hardware, since the programming of the hardware architectures embedded in AMMOD stations (especially multi-core computers and programmable hardware components) requires expert knowledge. It is a declared goal to provide a design methodology that automatically generates an optimized hardware-software configuration of the platform from an intuitive and graphical description of data processing algorithms, so that experts from other areas are also able to program the platform without the corresponding know-how.

  • Cyberkriminalität und forensische Informatik

    (Third Party Funds Single)

    Term: 1. October 2019 - 31. March 2024
    Funding source: Deutsche Forschungsgemeinschaft (DFG)
    URL: https://www.cybercrime.fau.de-

    Cyberkriminalität wird angesichts der wachsenden gesellschaftlichen Bedeutung der Informationstechnologie zu einer immer größeren Bedrohung. Gleichzeitig bieten sich neue Möglichkeiten der Strafverfolgung, wie etwa automatisierte Datensammlung und -auswertung im Netz oder Überwachungsprogramme. Doch wie geht man mit den Grundrechten der Betroffenen um, wenn „forensische Informatik“ genutzt wird? Das GRK „Cyberkriminalität und Forensische Informatik“ bringt Expertinnen und Experten der Informatik und Rechtswissenschaften zusammen, um das Forschungsfeld „Strafverfolgung von Cyberkriminalität“ systematisch zu erschließen.

Recent publications

2022

2021

2020

Related Research Fields

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