(Invited) Capturing Biological Behavior in Nanomagnetic Artificial Neurons and Synapses for Energy-Efficient Neuromorphic Computing

摘要

Modeling computer architecture after the human brain is a promising approach to the ever-increasing demands for real-time processing of massive amounts of data. But, the building blocks of the brain, i.e. neurons and synapses, have more detailed behavior than simply how they are connected, and this behavior is central to their computing efficiency. If we want to capture the energy efficiency of such a neuromorphic architecture, we need devices themselves that can capture the behavior of the biological elements.