Overview of memristor-based neural network design and applications
L Ye, Z Gao, J Fu, W Ren, C Yang, J Wen, X Wan… - Frontiers in …, 2022 - frontiersin.org
Conventional von Newmann-based computers face severe challenges in the processing
and storage of the large quantities of data being generated in the current era of “big data.” …
and storage of the large quantities of data being generated in the current era of “big data.” …
Voltage-controlled domain wall motion-based neuron and stochastic magnetic tunnel junction synapse for neuromorphic computing applications
This work discusses the proposal of a spintronic neuromorphic system with spin orbit torque-
driven domain wall motion (DWM)-based neurons and synapses. We propose a voltage …
driven domain wall motion (DWM)-based neurons and synapses. We propose a voltage …
Voltage-Gated Domain Wall Magnetic Tunnel Junction for Neuromorphic Computing Applications
We propose a novel spin-orbit torque (SOT) driven and voltage-gated domain wall motion
(DWM)-based MTJ device and its application in neuromorphic computing. Using the voltage …
(DWM)-based MTJ device and its application in neuromorphic computing. Using the voltage …
Voltage gated domain wall magnetic tunnel junction-based spiking convolutional neural network
We propose a novel spin-orbit torque (SOT) driven and voltage-gated domain wall motion
(DWM)-based MTJ device and its application in neuromorphic computing. We show that by …
(DWM)-based MTJ device and its application in neuromorphic computing. We show that by …
Unsupervised learning & reservoir computing leveraging analog spintronic phenomena
We have proposed three distinct spintronic neural network approaches that leverage analog
spintronic phenomena: 1) Unsupervised learning systems with spin-transfer torque …
spintronic phenomena: 1) Unsupervised learning systems with spin-transfer torque …
Intrinsic lateral inhibition facilitates winner-take-all in domain wall racetrack arrays for neuromorphic computing
Neuromorphic computing is a promising candidate for beyond-von Neumann computer
architectures, featuring low power consumption and high parallelism. Lateral inhibition and …
architectures, featuring low power consumption and high parallelism. Lateral inhibition and …
Plasticity-enhanced domain-wall MTJ neural networks for energy-efficient online learning
Machine learning implements backpropagation via abundant training samples. We
demonstrate a multi-stage learning system realized by a promising non-volatile memory …
demonstrate a multi-stage learning system realized by a promising non-volatile memory …
[PDF][PDF] CMOS-Free Spintronic Neural Network with Unsupervised Learning.
WH Brigner, N Hassan, CH Bennett, X Hu, A Velasquez… - 2021 - osti.gov
Von Neumann computers excel at processing information presented in a rigid, structured
format. However, due to the volatility of their constituent components, these machines are …
format. However, due to the volatility of their constituent components, these machines are …
[PDF][PDF] Lateral Inhibition and Winner-Take-All in Domain Wall Racetrack Arrays for Neuromorphic Computing.
C Cui, O Akinola, N Hassan, C Bennett, M Marinella… - 2020 - osti.gov
Neuromorphic computing is a promising candidate for beyond-von Neumann computer
architectures, featuring low power consumption and high parallelism. Lateral inhibition and …
architectures, featuring low power consumption and high parallelism. Lateral inhibition and …