Neuromorphic Computing


We investigate innovative cognitive brain-inspired systems that mimic the mammalian brain's information processing. We aim to develop a reconfigurable system that supports spike-based adaptation and several plasticity mechanisms based on online on-chip learning. Moreover, the system supports a sequence of processing tasks (i.e., a stream of events from sensors), produces intelligent behavior, and adapts to the environment. For a proof of concept, we have prototyped a reliable three-dimensional digital neuromorphic system geared explicitly toward the 3D-ICs biological brain's three-dimensional structure (R-NASH), where information in the network is represented by sparse patterns of spike timing and learning is based on the local spike-timing-dependent plasticity rule. R-NASH enables real-time and low-power solutions targeted at full-custom VLSI and FPGA integration.