Unsupervised Competitive Hardware Learning Rule for Spintronic Clustering Architecture
arXiv:2003.11120
Abstract
We propose a hardware learning rule for unsupervised clustering within a novel spintronic computing architecture. The proposed approach leverages the three-terminal structure of domain-wall magnetic tunnel junction devices to establish a feedback loop that serves to train such devices when they are used as synapses in a neuromorphic computing architecture.
References in corpus (3)
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- Shape-based Magnetic Domain Wall Drift for an Artificial Spintronic Leaky Integrate-and-Fire Neuron
- CMOS-Free Multilayer Perceptron Enabled by Four-Terminal MTJ Device