Neuromorphic Computing Algorithms and Systems


Neuromorphic computing uses spiking neuron network models to solve machine learning problems in a more power/energy-efficient way when compared to the conventional artificial neural networks.We research adaptive low-power spiking neuromorphic systems and SoCs empowered with our earlier developed fault-tolerant three-dimensional on-chip interconnect technology. In particular, we investigate innovative algorithms and neuromorphic systems, including adaptive configuration methods to enable the reconfiguration of different network parameters (spike weights, routing, hidden layers, topology, etc.), fault-tolerance, thermal-aware mapping methods, and on-line learning algorithms. The target applications include anthropomorphic robotics and edge computing.