Embedded AI

Research foci

• Innovative system approaches (HW&SW) through interdisciplinary research in Machine Learning, Signal Processing and Integrated Circuits Design
• Intelligent multisensor data processing and fusion as well as distributed reasoning, decision-making, automation and control to establish design principles for collectives of ultra-reliable and ultra-scalable autonomous systems.
• Machine Learning for formal methods and formal methods for Machine Learning for next order dependability of critical embedded SW and complete systems

Research competencies

• Neuromorphic architectures and neural networks HW/SW systems engineering
• Combining advance signal processing with Machine, Deep and Reinforcement Learning
• Analog, mixed-signal and digital integrated circuit design
• Sensor data processing, sensor fusion and perceptive computing with Machine Learning (including DL)
• Cyber-physical systems and wireless sensor & actuator networks
• Embedded real-time SW design
• embedded HW/SW systems engineering, embedded AI systems, GPU, FPGA and micro-controller programming

Beyond these competencies, the interdisciplinary approach we at SAL are following within and beyond research areas enables us to not only push enabling technologies to next levels, but even more importantly allows us to truly embrace challenges from various application fields.

Applications

• NNbased RF
• Radar Tomography
• selforganizing
  • multi-sensor arrays
  • autonomous vehicles and drones
  • robots for industrial applications
• Verification and Testing of embedded systems of highest complexity (eg Mobile Phone Field Testing,..., Autonomous Driving)

Watch video: Project Digineuron