61 citations · 66 across the 5 of their papers we have counts for
5 papers
Shape-Dependent Multi-Weight Magnetic Artificial Synapses for Neuromorphic Computing
Thomas Leonard, Samuel Liu, Mahshid Alamdar +8
In neuromorphic computing, artificial synapses provide a multi-weight conductance state that is set based on inputs from neurons, analogous to the brain. Additional properties of t…
Domain Wall-Magnetic Tunnel Junction Spin Orbit Torque Devices and Circuits for In-Memory Computing
Mahshid Alamdar, Thomas Leonard, Can Cui +8
There are pressing problems with traditional computing, especially for accomplishing data-intensive and real-time tasks, that motivate the development of in-memory computing device…
Unsupervised Competitive Hardware Learning Rule for Spintronic Clustering Architecture
Alvaro Velasquez, Christopher H. Bennett, Naimul Hassan +5
We propose a hardware learning rule for unsupervised clustering within a novel spintronic computing architecture. The proposed approach leverages the three-terminal structure of do…
Plasticity-Enhanced Domain-Wall MTJ Neural Networks for Energy-Efficient Online Learning
Christopher H. Bennett, T. Patrick Xiao, Can Cui +6
Machine learning implements backpropagation via abundant training samples. We demonstrate a multi-stage learning system realized by a promising non-volatile memory device, the doma…
Maximized Lateral Inhibition in Paired Magnetic Domain Wall Racetracks for Neuromorphic Computing
C. Cui, O. G. Akinola, N. Hassan +4
Lateral inhibition is an important functionality in neuromorphic computing, modeled after the biological neuron behavior that a firing neuron deactivates its neighbors belonging to…