We are developing low-power chips and SoCs with on-chip learning capabilities to tackle complex real-time machine learning applications such as robotic control, pattern recognition, and brain-machine interfaces…
Our research tackles adaptive systems and SoSs targeted for complex real-time machine learning applications such as robotic control, pattern recognition, and brain-machine interfaces…
Our applications range from neuro-inspired (brain-like) low-power computing embedded systems to adaptive neural-based control, and brain-machine interfaces
Research at the Adaptive Systems Laboratory focuses on energy-efficient adaptive computing systems based on novel adaptive algorithms and fault-tolerant scalable interconnects to overcome the limitation of traditional stored-program computing style. In particular, we study adaptive computing systems for …
We investigate innovative computing systems and technologies that deliver high performance, energy efficiency, and adaptivity for various application domains and social needs. In particular, we develop algorithms and techniques for run-time decision-making targeting various optimization goals such as power/energy-efficiency, fault-tolerance/reliability, or compute performance, and targeting different architectural platforms, including high-performance computing nodes, reconfigurable systems, and power-constrained edge computing systems and technologies.
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