R&D Lab »Next Generation Computing«

The next generation of computing will be based on three different pillars: classical architectures, analog and neuromorphic computing, and quantum computing. The focus of the »Next Generation Computing« (NGC) lab team is on neuromorphic and quantum computing. The researchers are working on new computer and memory architectures for embedded systems as well as high performance computing. The applications under consideration range from control units for autonomous driving to AI-supported ECG analyses in medical technology.

These systems can be implemented in a variety of architectures and implementation styles, such as programmable hardware devices (FPGAs), application specific integrated circuits (ASICs), or application specific instruction set processors (ASIPs). Therefore, simulation models, called virtual prototypes, are used early on to enable targeted hardware-software co-design and exploration of the design space.

The lab also deals with platform architectures for neuromorphic accelerators as well as finding so-called spiking neural networks (SNNs) that are as optimally adapted as possible to the hardware as well as to applications.

In the area of quantum computing, the lab is concerned with hybrid systems, generic software stacks, and benchmarking on demonstrators. 

Detail of a quantum computer
© IBM Research
Quantum computers play a central role in Next Generation Computing.

Current projects in the area of Next Generation Computing

  • MEMTONOMY: Optimization of Memory Systems for Autonomous Driving
  • HALF2: Energy-efficient electronics for AI-supported ECG analysis in telemedicine
  • GreenDT: CO2 efficient simulation
  • SEC-learn Arrival: Embedded neuromorphic computing platform for federated learning
  • Enerquant: Benchmarking and modeling energy models for quantum computing
  • EniQmA: Hybrid systems: collaboration on use cases, hybrid quantum HPC algorithm development
  • Rymax, Spinning: demonstrator setups of quantum computers
  • SIKRIN-KRYPTOV: Securing hydraulic systems in critical infrastructures by developing and implementing quantum computer-resistant cryptographic methods
  • FunkI: Radio communication with artificial intelligence
  • ZuSE-KI-AVF: Application-specific AI processor for intelligent sensor signal processing in autonomous driving
  • DRAMSys: Tool for optimizing memory systems through simulation analysis
  • DRAMMeasure: Measurement platform for DRAM memories


We work together with Bosch, Creonic, Daimler, Equinor, IBM, LUBIS, Rambus, SiPearl, Total Energies, XILINX, Zollner, among others.



InnoVisions / 28.6.2022

Quantum Computing for the Big Picture

Can quantum computers calculate Germany's entire energy system? Determining the exact amount of energy needed at that moment is almost an impossibility, especially for conventional computers. But perhaps it can be done with quantum computers? The project EnerQuant wants to clarify what opportunities opens up here. [Article only available in German]


InnoVisions / 21.6.2022

Faster with Quantum Algorithms

The development of new composite materials is precision work. The micrometer-sized structures of the high-performance materials must later ensure the functionality, durability and safety of complex components for aircraft fuselages, wind turbine rotors or in buildings. We already have excellent mathematical methods. One problem lies with computation time. Is the solution quantum computing? Will it soon make everything much faster? [only available in German]


Press release / 10.12.2021

Development of a Quantum Computer

The German Federal Ministry of Education and Research is funding the RYMAX project to develop a practice-ready quantum computer. The total project volume amounts to 29 million euros. The University of Kaiserslautern-Landau receives 7 million euros of this.

Press Release / 16.03.2021

Innovation competition »Energy-efficient AI system« of the BMBF

Success for the Half2 project

The pilot innovation competition of the German Federal Ministry of Education and Research (BMBF) called for the development of a powerful chip on which artificial intelligence (AI) algorithms can be executed. Using an application example from the field of medicine, this system was to recognize the clinical picture of atrial fibrillation with a high degree of reliability from ECG data.

The winning team around the Half2 project now has the opportunity to realize its solutions and to transfer the AI algorithms into practice.