Chair of Business Informatics
Saarland University

Strong coverage of the presentation of the project ESCADE at the Hannover Exhibition 2025

https://www.chip.de/news/KI-Einheiten-verbrauchen-sehr-viel-Energie-Wissenschaftler-steigern-nun-die-Effizienz-um-90-Prozent_185865935.html

https://www.automation-next.com/future-tech/7-spannende-forschungsthemen-auf-der-hannover-messe-387.html

https://www.cloudmagazin.com/2025/03/21/forschende-arbeiten-an-bis-zu-90-prozent-effizienteren-ki-rechenzentren/

https://www.konstruktionspraxis.vogel.de/wie-ki-vom-energiefresser-zum-energiesparer-wird-a-a5f53f974957b2993ffc5c4864220316/?cmp=beleg-mail&pt=67d925aaf2944

https://www.elektrotechnik.vogel.de/wie-ki-vom-energiefresser-zum-energiesparer-wird-a-39d6c8b6b0c16724ae0b984b6a9a7176/?cmp=beleg-mail&pt=67da82e682c96

https://mit-blog.de/nachhaltige-rechenzentren-wie-ki-um-bis-zu-90-prozent-energieeffizienter-wird/

https://www.industr.com/de/ki-soll-vom-energiefresser-zum-energiesparer-werden-2784677

https://www.innovations-report.de/medien-ereignisse/hannover-messe/hannover-messe-ki-macht-rechenzentren-90-energieeffizienter/

https://smartup-news.de/technologie/ki-frisst-strom-ohne-ende-forscher-machen-sie-energieeffizienter/

https://www.sr.de/sr/home/nachrichten/politik_wirtschaft/hannover_messe_aussteller_saarland_100.html

Lecture on Data Science at Universität des Saarlandes is going to start this week. Don't miss it. This lecture targets students in business informatics, computer science, bioinformatics, computer linguistics, and beyond. After last year collaboration with Bosch and HYDAC Group, this time projects will be conducted in collaboration with the Boston Consulting Group (BCG).

Streamlining LLMs: Adaptive Knowledge Distillation for Tailored Language Models

Prajvi Saxena, Sabine Janzen, Wolfgang Maass:

Large language models (LLMs) like GPT-4 and LLaMA-3 offer transformative potential across industries, e.g., enhancing customer service, revolutionizing medical diagnostics, or identifying crises in news articles. However, deploying LLMs faces challenges such as limited training data, high computational costs, and issues with transparency and explainability. Our research focuses on distilling compact, parameter-efficient tailored language models (TLMs) from LLMs for domain-specific tasks with comparable performance. Current approaches like knowledge distillation, fine-tuning, and model parallelism address computational efficiency but lack hybrid strategies to balance efficiency, adaptability, and accuracy. We present ANON - an adaptive knowledge distillation framework integrating knowledge distillation with adapters to generate computationally efficient TLMs without relying on labeled datasets. ANON uses cross-entropy loss to transfer knowledge from the teacher's outputs and internal representations while employing adaptive prompt engineering and a progressive distillation strategy for phased knowledge transfer. We evaluated ANON's performance in the crisis domain, where accuracy is critical and labeled data is scarce. Experiments showed that ANON outperforms recent approaches of knowledge distillation, both in terms of the resulting TLM performance and in reducing the computational costs for training and maintaining accuracy compared to LLMs for domain-specific applications.

Energy-efficient artificial intelligence for sustainable data centers

The research project ESCADE is presented at Hannover Messe 2025 from March 31 to April 4 at the booth of the Federal Ministry for Economics and Climate Change (Hall 2, Booth A18).

The climate neutrality goals, rising energy costs and the limitations of traditional hardware are jeopardizing the economic viability of German data centers, which are vital for the digital transformation using AI. Their massive energy consumption (16 billion kWh in 2020) is set to be reduced through the use of neuromorphic hardware and energy-efficient AI algorithms.

ESCADE optimizes the energy efficiency of AI in data centers. Approaches such as knowledge distillation, quantization and neural architecture search result in smaller, more energy-efficient AI models that nevertheless deliver a comparable performance. When combined with neuromorphic hardware, these models can achieve energy savings of up to 90%.

An ESCADE AI agent optimizes the energy efficiency of algorithms throughout their development, training and deployment. By making data-driven decisions, it enables resource-efficient solutions, for example in scrap sorting and software development, without performance loss. Small and medium-sized enterprises, in particular, benefit from AI that is both economically and environmentally sustainable.

The ESCADE exhibit visualizes the project results in the form of a physical table demonstrator. The technical prototype EAVE - Energy Analytics for Cost-Effective & Sustainable AI Operations empowers decision-makers with real-time insights into the energy efficiency of AI workloads. The tool supports measuring, predicting and optimizing operational costs, energy-efficiency and CO2 emissions of AI models including the optimization of data center power usage effectiveness (PUE). The EAVE measurement module analyzes current cost- and energy-efficiency, while the prediction module enables analysis of future AI workloads with respect to cost and energy. The optimization module analyses in-depth AI model optimization, focusing on compression techniques for Large Language and Vision Models in the context of industry use cases.

ESCADE is funded from 01.05.2023 - 30.04.2026 by Federal Ministry for Economics and Climate Change (BMWK). The research team of Prof. Maaß at German Research Center for Artificial Intelligence (DFKI) is coordinating the project and collaborates with the funded partners NT Neue Technologie AG, Stahl-Holding-Saar GmbH & Co. KGaA, SEITEC GmbH, Technical University Dresden, University Bielefeld and the Austrian Partner Salzburg Research.

Advancing Quantum Computing for Manufacturing Simulation

The final meeting of the research project QUASIM: QUANTUM COMPUTING ENHANCED SERVICE ECOSYSTEM FOR SIMULATION IN MANUFACTURING took place at the German Research Center for Artificial Intelligence (DFKI) in Saarbrücken. This collaborative project aimed to explore how quantum computing can enhance simulation technologies in the manufacturing sector, particularly within the metalworking industry.

With over 390,000 companies and approximately 3.7 million employees, the metalworking industry represents the largest secondary sector within the EU-28 (Eurostat, Sectoral Analysis of Key Indicators). Machining is one of the most significant manufacturing technologies in this sector, crucial for industries such as tool and mould making, the semiconductor industry, and engine construction. Given the industry's reliance on machining, companies are continuously striving for higher quality, productivity, cost-effectiveness, and sustainability.

Quantum Computing for Manufacturing Optimization

State-of-the-art manufacturing processes can be optimized with the support of computer simulations, which are often time-consuming and expensive. QUASIM focuses on mathematical problems that frequently arise in industrial simulations, developing quantum algorithms that can solve these problems faster or with less computational storage than classical approaches. The research aims to improve scalability based on problem-dependent parameters, such as the number of equations to solve or the desired accuracy.

By considering large-scale mathematical problems common in manufacturing simulations, QUASIM aims to revolutionize computational efficiency and industrial applicability.

Use Cases of Quantum Computing in Manufacturing

Use Case 1: Milling Process Optimization

Milling is a metal-cutting manufacturing process where a multi-toothed tool rotates to create various workpiece surfaces. Unlike other processes such as turning or drilling, milling features constant cutting interruptions, which can cause dynamic excitations leading to vibration marks on the workpiece surface. To address this issue, dynamic process stability simulations are performed to analyze vibrations and optimize milling process design, particularly for thin-walled components.

In QUASIM, a dexel-based engagement simulation is followed by a finite element method (FEM) simulation workflow, involving:

- IPW (in-process workpiece) conversion from dexel to solid

- Meshing process

- Modal analysis

A key challenge is the solution of eigenvalue problems in modal analysis, which are computationally intensive. Quantum algorithms are being investigated to accelerate eigenvalue problem solving, potentially reducing simulation time and improving accuracy in milling process optimization.

Use Case 2: Optimizing Laser Cutting Through Thermal Expansion Analysis

In laser cutting, accurately predicting and managing thermal expansion in metal sheets is critical to ensuring precision and preventing defects. The heat generated by the laser causes thermal deformation, which affects the final shape and quality of the manufactured component. Simulations are essential for modeling these effects, but traditional finite element method (FEM)-based simulations are computationally expensive.

QUASIM investigates quantum-enhanced approaches for thermal expansion analysis, focusing on solving large-scale differential equations governing heat transfer and deformation. Quantum algorithms are being explored to:

- Accelerate the solution of heat diffusion equations

- Improve the accuracy of temperature distribution predictions

- Enhance optimization of laser parameters for high-precision cutting

By leveraging quantum machine learning (QML) techniques, the project aims to develop faster and more accurate thermal simulations, enabling manufacturers to reduce waste, increase precision, and improve energy efficiency in laser-based manufacturing processes.

Quantum Computing as a Game Changer

Initial research within QUASIM suggests that quantum computing offers significant advantages in addressing these challenges. Quantum mechanical principles have the potential to:

- Accelerate numerical simulations dramatically

- Improve simulation accuracy through quantum machine learning (QML)

- Enable real-time optimization of machining processes

These findings indicate that quantum-enhanced computing could revolutionize manufacturing by making high-fidelity simulations feasible for industrial use.

Participants and Key Stakeholders

The final meeting brought together key stakeholders from academia, industry, and government, reflecting the project's broad impact and interdisciplinary nature. Notable participants included:

- Wolfgang Förster, Staatssekretär des Finanz- und Wissenschaftsministeriums des Saarlandes

- Dr. Glasmacher (BMWK)

- Dr. Grass (DLR)

- Prof. Frank Wilhelm-Mauch (FZJ)

- Prof. Wolfgang Maaß (DFKI, Project Coordinator)

- Dr. Tobias Stollenwerk (FZJ)

- Dr. Valentina König (moduleworks)

- Sven Danz (FZJ)

- Alejandro Delgadillo (moduleworks)

- Marco Kulig (TRUMPF)

- Rivan Ruguhbar (FZJ)

- Anika Rusch (DFKI)

- Stefan Schröder (Fraunhofer IPT)

- Nirav Shinoy (DFKI)

- Hannah Stein (DFKI)

The meeting marked the culmination of years of research and innovation, demonstrating the potential of quantum computing to transform manufacturing simulation. As the industry moves forward, continued collaboration between research institutions, technology providers, and manufacturers will be essential in bringing these advancements from the lab to real-world applications.

Mobility in rural areas often revolves around individualized solutions, such as private cars, creating challenges such as traffic congestion, environmental strain, and restricted access for socially disadvantaged and physically impaired individuals. To address these issues, the INTE:GRATE project is setting out to transform mobility in Saarland by offering groundbreaking transparency and data-driven decision support for policymakers, businesses, and public transportation providers.

The project aims to analyze and understand the current state of system transport in Saarland, offering insights into the factors driving strong individualization in rural mobility. By leveraging innovative AI-based services, INTE:GRATE will provide actionable recommendations to shift from individual to system-oriented mobility, focusing on inclusivity and efficiency.

Through aggregating diverse data streams—including movement data, demographic trends, mobility app usage, infrastructure data, and economic indicators—INTE:GRATE will distill this information into district-level insights. The project will identify mobility patterns and influencing factors, enabling smarter traffic bundling, enhanced mobility efficiency, and strengthened social and cultural participation, particularly for marginalized groups.

Key objectives of the INTE:GRATE project include:

- A detailed analysis of current transport systems in Saarland.

- Development of AI-driven decision support tools to guide stakeholders.

- Tailored recommendations to improve system mobility and ensure equal access for all citizens, regardless of their physical or social status.

Presentation of Results at the Saarland State Parliament

The project reached an important milestone today as its preliminary results were presented to the Transport Committee of the Saarland State Parliament by Prof. Wolfgang Maaß. This presentation marked a significant step in fostering dialogue with policymakers and stakeholders, providing them with actionable insights and recommendations to drive meaningful change in regional mobility.

The outcomes of this initiative promise a mobility revolution in Saarland, paving the way for a sustainable, inclusive, and efficient transportation future.

Collaboration and Funding

The INTE:GRATE project is a collaborative effort with the HTW University of Applied Sciences and is funded by the European Regional Development Fund (EFRE) of the European Union.

Berchtesgaden, January 2025 – At the prestigious valantic Digital Excellence Forum 2025, Professor Wolfgang Maaß delivered an insightful keynote on the future of artificial intelligence in Europe and its profound impact on the economic landscape. The event brought together industry leaders, AI researchers, and digital transformation experts to discuss cutting-edge developments in AI and their implications for businesses and society.

Professor Maaß began his talk with an overview of the industrial and scientific state of the art in AI, highlighting key advancements in deep learning, reinforcement learning, and explainable AI. He emphasized how AI is no longer an emerging technology but a critical driver of industrial innovation and economic transformation.

A major highlight of his presentation was the discussion on Deepsearch R1, an AI breakthrough that had been released just days prior. Professor Maaß provided an analysis of its capabilities and potential applications, demonstrating how such advancements are accelerating the integration of AI into business intelligence, automation, and research processes.

Following this, he turned to AI agents and their increasing importance across industries. He explained how AI-driven autonomous systems are reshaping supply chains, financial markets, healthcare, and manufacturing, driving efficiency and innovation. He emphasized that companies need to adapt quickly to leverage AI’s potential, or risk falling behind in a rapidly evolving digital economy.

The final section of his talk focused on the future of AI and Quantum Machine Learning (QML). Professor Maaß outlined how QML could revolutionize optimization problems, material discovery, and financial modeling, paving the way for unprecedented computational capabilities. While quantum computing is still in its early stages, he stressed that companies and researchers should already be preparing for its transformative effects.

Concluding his presentation, Professor Maaß offered an optimistic outlook on Germany’s position in AI research and startups. He highlighted the strong academic foundations, vibrant AI ecosystem, and increasing investments in AI-driven ventures, positioning Germany as a key player in global AI innovation.

The valantic Digital Excellence Forum 2025 proved to be a crucial platform for discussing the future of AI in Europe. Professor Maaß’s keynote left attendees with valuable insights into the rapid advancements in AI, the strategic importance of AI agents, and the transformative potential of quantum computing. His final remarks reinforced the positive trajectory of European AI development, urging businesses and policymakers to embrace AI-driven opportunities for sustainable economic growth.

https://www.valantic.com/en/events/def2025/

Three papers at NeurIPS 2024, Vancouver, Canada. NeurIPS is one of the leading AI conferences with about 16.000 participants.

(1) Maxx Richard Rahman, Ruoxuan Liu, and Wolfgang Maass: Incorporating Metabolic Information into LLMs for Anomaly Detection in Clinical Time-Series

(2) Prajvi Saxena, Sabine Janzen and Wolfgang Maass: Adaptive Knowledge Distillation for Efficient Domain-Specific Language Models.

(3) Cicy Kuriakose Agnes and Wolfgang Maass: From Stateless to Adaptive: Dynamic Personalization for Conversational Language Models.

Thousands of athletes are currently competing for medals at the Olympic Games in Paris. And in some cases, questions will be asked about whether medals were won fairly or whether doping was involved. Software developed by a team led by Wolfgang Maaß, professor of business informatics at Saarland University, could help to answer these questions in future competitions. The software, which is currently being presented at the International Joint Conference on AI (3–9 August in South Korea), needs only a handful of data points to predict with unprecedented accuracy which athletes have definitely not doped – and can thus identify those cases where a closer look is required.

We are happy to inform you that we will be presenting three of our research projects, all funded by Federeal Ministry for Economics and Climate Action (BMWK) from April 22 – 26 at the prestigious Hannover Messe. Find us at booth B10 in hall 2.