Innovation Center Prof. Dr.-Ing. habil. Christian Mayr

Neuromorphic systems

The Neuromorphic Systems Innovation Center focuses on the development of energy-efficient systems-on-chip (SoC) for applications in artificial intelligence, brain simulation and neuroprosthetics. The focus is on bio-inspired circuit concepts and the integration of neuronal networks in CMOS technologies.

Portrait of Prof. Christian Mayr

MANAGEMENT

Prof. Dr.-Ing. habil. Christian Mayr

Dresden University of Technology
Institute for Fundamentals of Electrical Engineering and Electronics
Professorship for Highly Parallel VLSI Systems and Neuromicroelectronics
Helmholtzstraße 10
01069 Dresden

Telefon +49 351 463 42392
E-Mail christian.mayr@tu-dresden.de

Short vita

Career and professional stations:

  • Since 2015: Head of the Professorship for Highly Parallel VLSI Systems and Neuromicroelectronics at TU Dresden
  • Research stay at the Institute for Neuroinformatics at the University and ETH Zurich (2013-2015)
  • Habilitation at the Technical University of Dresden (2012)
  • Research assistant at the Chair of Highly Parallel VLSI Systems and Neuromicroelectronics, doctorate in engineering (2008)
  • Studied electrical engineering and information technology at the Technical University of Dresden, graduated as Dipl.-Ing. (2003)

Research and development

  • Development of the world's first neuromorphic system-on-chip in a 28nm CMOS technology
  • Development of mixed-signal interfaces for brain-machine interfaces
  • Modeling and IC design for synaptic plasticity and learning
  • Development of the second generation of the SpiNNaker platform for brain simulation and artificial intelligence

Expertise

  • Bio-inspired IC systems
  • Bio-inspired artificial intelligence and brain-computer interfaces
  • Mixed-signal VLSI design
  • Neuromorphic computing

Reference projects

  • Software for human-robot interaction
  • Investigation of the applicability of a UCIe communication standard for AI chiplet systems
  • Conceptual studies on the analysis of trace data with AI methods
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