Jean Anne C. Incorvia, Ph.D, University of TX - Austin
Neuromorphic computing, i.e. 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.
Perhaps surprisingly, magnetic materials and devices have many properties similar to the brain that make them attractive candidates as artificial neurons and synapses. I will present our recent work on designing and modeling three-terminal magnetic tunnel junctions that can act as both leaky, integrate, and fire neurons with inherent lateral inhibition , and as synapses with online learning and spike-timing dependent plasticity behavior . I will show we can fabricate the devices with a high on/off ratio over 160%. This work could lead to a monolithic platform for neuromorphic computing using magnetic materials.
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