Summer Semester 2026

Grundlagenpraktikum: Rechnerarchitektur

Content:

Over the first half of the course, lecture videos, tutorials, and homework are used to teach fundamental topics in computer architecture, including:

• Introduction to C Programming
• Secure programming
• Debugging tools and optimization strategies
• System Design
  • Planning and reading of combinational and sequential circuits
  • Realization of circuits using the SystemC library for C++
  • Optimization strategies for circuits
• Assembly Language Programming
  • Usage of x86 assembly language
  • Optimization of C programs

In the second half of the semester, students work on a practical project relating to one of the two branches (System Design and Assembly Language Programming). Projects are implemented in groups of three and finished with a presentation at the end of the semester.

VI Introduction to Emerging Computing Technologies

Lecturer(s): Robert Wille, Marcel Walter

Content:

Computer technologies will change in the near future. The exponential growth of conventional technologies (according to Moore’s Law) will come to a halt since physical boundaries will be reached soon. At the same time, further system concepts beyond pure electronics emerge. As a consequence, researchers and engineers are currently considering alternative (emerging) computer technologies that work differently from established (conventional) computation paradigms. Examples include quantum computing, reversible circuits, microfludic devices (also known as Labs-on-a-Chip), or field-coupled nanotechnologies. This module provides an overview of these technologies and the corresponding paradigms. This covers an introduction to the respective concepts as well as possible applications. Afterward, questions of how to efficiently design applications/solutions for these technologies are discussed.

SE Seminar on Topics in Design Automation

Lecturer(s): Robert Wille

Content:

In this seminar, current topics from the area of Design Automation are discussed among the participants. A structured introduction into scientific literature regarding paper reading, literature research, presentation techniques, and scientific writing is given. The participants are enabled to independently perform all required steps to present a scientific topic in form of a review paper and an oral presentation.

Machine Learning for Electronic Design Automation and Manufacturing

Lecturer(s): Lorenzo Servadei, Robert Wille

Content:

The complexity of modern chips significantly impacts the cost and capabilities of design and manufacturing for traditional Design Automation Toolkits. This issue is exacerbated by the increased relevance of software and applications running on modern SoCs and their co-design. Current Design Automation methodologies often struggle to fully capture and optimize complicated designs or reduce them to the initial specification. However, advancements in data-driven algorithms, particularly in Machine Learning, can address these shortcomings. This course teaches how to apply Machine Learning to enhance and improve the chip design process.

This module provides an in-depth exploration of machine learning for design automation, including:

• theory and application of machine learning algorithms,
• in-depth exploration of recent machine learning methods suitable for design automation tasks,
• overview of topics, areas, and current industrial pain points in the semiconductor chain where a great availability of data exists,
• detailed coverage of data structures and state-of-the-art tools for tackling design automation, as well as
• hands-on experiences of machine learning algorithms applied to design automation tasks.

Logic Synthesis and Physical Design

Lecturer(s): Marcel Walter, Robert Wille

Content:

Modern computer chips consist of billions of transistors, making them some of the most complex systems ever created by humans. How does one design such intricate architectures? The answer is algorithms developed and fine-tuned over decades. In this course, students will learn about the techniques that automatically obtain computer chip designs from specifications. To this end, we will explore logic synthesis and optimization as well as partitioning, floorplanning, placement, and routing. Many of these algorithms are meta-heuristics that can be applied in completely different fields, too, like resource allocation, city planning, logistics, compilers, etc. Additionally, students will gather hands-on experience with state-of-the-art tools in logic synthesis and physical design, with the opportunity to participate in an international contest.

The students will get to know data structures and algorithms, and will be able to implement them, e.g.,

• AIGs, MIGs, XAGs, kLUT networks,
• logic optimization and technology mapping,
• combinational equivalence checking,
• floor planning,
• global and detailed placement,
• global and detailed routing,
• legalization.

Additionally, the students will learn to operate open-source industrial-strength tools in the field, like ABC, Yosys, OpenROAD, or iEDA.

PR Quantum Computing Software Lab

Lecturer(s): Robert Wille, Laura Herzog

Content:

Quantum computers are becoming a reality. They offer new and powerful paradigms while also introducing new challenges, which, in turn, need different tools and methods to develop with the new paradigms. Accordingly, the design and realization of corresponding quantum computing solutions differs significantly from the conventional design and, hence, requires new software solutions and design methods. This module provides an introduction into the quantum computing paradigms as provides hands-on experiences on corresponding software tools and design automation methods. Different frameworks focused on quantum computing are presented and used to realize selected quantum algorithms in practice.

SE Quantum Entrepreneurship Laboratory

Content:

Quantum technologies are emerging as a new computing paradigm with significant potential for science and industry. However, transforming advances in quantum research into practical applications and commercial products requires interdisciplinary collaboration between technical and business experts. This course introduces students to this process by combining applied quantum research with entrepreneurial thinking.

In the first part of the course, students with technical backgrounds work in small teams on industry-relevant research topics in quantum computing and quantum-inspired technologies. These topics may include quantum software tools, compilation techniques, quantum-inspired machine learning methods, or applications of quantum technologies in data science and optimization. Students explore the underlying concepts, evaluate possible research directions, and develop prototype solutions or proof-of-concept implementations.

In the second part of the course, business students join the teams to form interdisciplinary groups. Together, the teams analyze the commercial potential of the developed technologies, assess market opportunities, and design potential product and business models. The course concludes with a startup-style pitch presented to a panel of academic and industry experts. Throughout the semester, invited talks and workshops from researchers, entrepreneurs, and industry partners provide insights into the current quantum technology ecosystem and the challenges of developing deep-tech startups.

Students will gain experience in interdisciplinary teamwork and project-based research and will learn to:

• analyze research ideas for technological feasibility and industry relevance,
• develop prototype solutions for quantum and quantum-inspired technologies,
• evaluate commercialization pathways and market opportunities,
• design business models for deep-tech innovations,
• communicate complex technical ideas to both technical and non-technical audiences.