Search Results for author:
Found 6 papers, 0 papers with code
High-Speed CMOS-Free Purely Spintronic Asynchronous Recurrent Neural Network
Neuromorphic computing systems overcome the limitations of traditional von Neumann computing architectures.
Passive frustrated nanomagnet reservoir computing
We therefore propose a reservoir that meets all of these criteria by leveraging the passive interactions of dipole-coupled, frustrated nanomagnets.
Domain Wall Leaky Integrate-and-Fire Neurons with Shape-Based Configurable Activation Functions
This work proposes modifications to these spintronic neurons that enable configuration of the activation functions through control of the shape of a magnetic domain wall track.
Unsupervised Competitive Hardware Learning Rule for Spintronic Clustering Architecture
We propose a hardware learning rule for unsupervised clustering within a novel spintronic computing architecture.
Plasticity-Enhanced Domain-Wall MTJ Neural Networks for Energy-Efficient Online Learning
Machine learning implements backpropagation via abundant training samples.
CMOS-Free Multilayer Perceptron Enabled by Four-Terminal MTJ Device
Neuromorphic computing promises revolutionary improvements over conventional systems for applications that process unstructured information.
