Comparing the biological efficiency of brains with the calculation efficiency of
traditional, von Neumann computing architectures yields a vast difference,
especially for certain computational tasks important for feature recognition and
AI applications. Inspired by evolved biological principles, the research
includes investigation on new computing approaches from physical and
mathematical principles (e.g.
ANN,
SNN,
ONN). However, mapping these approaches on conventional hardware loses some of the
benefits these paradigms provide. Emerging devices, like memristors, are
extremely promising but need to be combined with the well-established benefits
of modern CMOS-based technologies to keep the potential of all components of
system.
Hence, our group focuses on integrated circuit systems that leverage the maximum potential of bio-inspired computing paradigms and elements on a system level. With this research, we provide a crucial contribution to lift fundamental research into a higher readiness level. It allows us to benchmark the systems in a laboratory environment and bring it closer to commercialization.
At its core, we are using commercial CMOS technologies to build the foundation of integrated circuits. This is then enhanced by emerging devices, either from internal, yet experimental post-processing steps or from other available prototype lines. The resulting complexity of the systems requires a contribution from several interdisciplinary experts. Therefore, projects in this field will most likely be part of bigger endeavors, spanning over several groups with a significant contribution to integrated circuit design from our team.
