Researchers fuse novel devices with biological inspiration for future AI systems

The SNN approach uses biologically inspired, event-driven spike-based computation and communication -- meaning only operating when needed -- in its design. One of the distinguishing features of SNN as a computing paradigm is the integration of the element of time into algorithms and models. Penn State scientists are exploring novel magnetic device structures to directly mimic such temporal non-linear characteristics in hardware, scalable architecture and interconnection fabrics for these devices, along with novel hybrid algorithm designs to leverage the benefits of both SNN models and traditional non-spiking deep learning models.