Mathematikon Entrance

Our Faculty is the academic home of researchers, teachers, and students of Mathematics and Computer Science. Its institutes and facilities are housed in the Mathematikon, pleasantly located on the Campus Neuenheimer Feld of Heidelberg University. Welcome!

Mathematikon Seminar Room

The Doctorate signifies a proven ability to conduct independent scientific research. Under the auspices of the Combined Faculty of Natural Sciences and Mathematics, we confer over 30 Doctoral degrees in the subjects of mathematics and computer science each year.

Mathematikon Library

Students interested in Mathematics, Computer Science, or an interdisciplinary field, pursuing a B.Sc., M.Sc., or M.Ed., and aiming for a career in research, reaching, or the private sector, will find here in Heidelberg a full range of first-class courses for a challenging and enriching educational experience in an intellectually stimulating environment with historical cachet.

Mathematikon Lobby

We seek to promote the interest in mathematics and computer science by organizing events for schools and for the broader public. Alumns and newcomers join in and contribute to shared knowledge and contacts.

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Hero Computer Eng
Mathematics and Computer Science — Research

Computer Engineering

Computer Engineering deals with the operation principles, development and utilization of hardware for acquiring and processing data. Through a thorough understanding of the elementary components, functional units, chips, computer architectures and of large, heterogeneous computing systems it is possible – through ‘hardware aware programming’ – to adapt algorithms to the strengths and weaknesses of the available hardware and to significantly increase compute performance and power efficiency. Specific computing tasks are addressed by application specific hardware/software combinations and by optimized system configurations.

Computer Engineering in Heidelberg has links to many research fields in the university where large amounts of data must be acquired or processed quickly. ZITI has the competences to develop electronics microchips for specific applications in sensing or data transport from scratch. Data communication being one of the largest sources of power consumption, is addressed on the hardware level, by hardware aware programming and adapted system design. Apart from efficient programming of modern processors, ZITI operates a variety of coprocessors (FPGA, GPU, ML-coprocessors) in order to exploit their specific strengths and research methods and workflows for most efficient data processing.

Circuit Design

Circuit Design

At the chair of Circuit Design, microelectronic circuits are developed, tested and applied. These microchips often contain extremely sensitive, low noise amplifiers for capturing sensor data and blocks for further analog and digital signal processing. Some chips contain particle- or (single) photon-sensitive structures. The crucial analogue parts of such chips are designed completely manually. Complex digital blocks described by HDL languages are converted to a layout by a suitable tool chain and both parts are merged. The chips are fabricated in state-of-the-art CMOS technologies and commissioned by suited additional hardware (often FPGA boards) in the group.

Examples projects are readout and processing chips for particle detectors (e.g. see picture), single photon sensors for dark matter search or microscopy, hybrid pixel detectors for synchrotron X-ray detection at Eu-XFEL or ESRF or chips for state-of-the-art PET scanners.

Computing Systems

Computing Systems

Today, research in computing systems is most concerned with specialized forms of computing in combination with seamless integration into existing systems. Specialized computing, for instance based on GPUs (as known for gaming) or FPGAs (field programmable gate arrays) or ASICs (not the shoe brand but “application-specific integrated circuits”), is motivated by diminishing returns from CMOS technology scaling and hard power constraints. Notably, for a given fixed power budget , energy efficiency defines performance : . Thus, a sustained performance scaling based on CMOS technology requires to improve the energy efficiency of compute and memory operations substantially, which is typically being done using the previously mentioned specialized forms of computing. However, any specialization stands in contrast to generality, thus raising various questions related to programmability and algorithmic innovation.

Particular research foci include

  • resource-efficient ML such as model compression for edge, mobile and embedded systems
  • code analysis and generation as for instance based on CLANG/LLVM and targeting (multi-)GPU systems
  • HW/SW codesign to meet application objectives by a comprehensive treatment of software and hardware components
  • specialized processor architectures under performance, energy efficiency and programmability constraints

The group is most concerned with bridging the gap in between application and hardware, including automated tools as well as abstract models that facilitate reasoning about various optimizations and decisions.

Application Specific Computing

Our research focuses on significant improvements of performance and accuracy in application specific computing through a global optimization across the entire spectrum of numerical methods, algorithm design, software implementation and hardware acceleration.

These layers typically have contradictory requirements and their integration poses many challenges. For example, numerically superior methods expose little parallelism, bandwidth efficient algorithms convolve the processing of space and time into unmanageable software patterns, high level language abstractions create data layout and composition barriers, and high performance on today's hardware poses strict requirements on parallel execution and data access. High performance and accuracy for the entire application can only be achieved by balancing these requirements across all layers.

The following topics are given particular attention:

  • Data representation (mixed-precision, compression, redundancy)
  • Data access (layout, spatial and temporal locality)
  • Data structure (unstructured grids, graphs, adaptivity)
  • Numerical methods (ILU, Krylov, GMG, AMG)
  • Programming abstractions (CUDA, thrust, PSTL, C++2x, UPC++)

Research Group Leaders

Prof. Dr. Peter FischerInstitute of Computer Engineering

Design of Microelectronic circuits and sensor systems for particle and photon detection

Prof. Dr. Peter Fischer
Prof. Dr. Holger FröningInstitute of Computer Engineering

Future and emerging technology (GPUs, FPGAs, ASICs, resistive RAM) for high-performance computing, resource-constrained machine learning and data analytics

Prof. Dr Holger Fröning
Prof. Dr. Robert StrzodkaInstitute of Computer Engineering

Parallel algorithms and hardware (GPU, many-core CPU, FPGA) in relation to data representation, data access, data structure, numerical methods and programming abstractions

Prof. Dr. Robert Strzodka
Last updated on Sep 28, 2021 at 11:13 AM