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Found 6 papers, 0 papers with code
Deep Neuromorphic Networks with Superconducting Single Flux Quanta
Neuromorphic circuits are a promising approach to computing where techniques used by the brain to achieve high efficiency are exploited.
Neuromorphic Hebbian learning with magnetic tunnel junction synapses
Neuromorphic computing aims to mimic both the function and structure of biological neural networks to provide artificial intelligence with extreme efficiency.
Experimental Demonstration of Neuromorphic Network with STT MTJ Synapses
We present the first experimental demonstration of a neuromorphic network with magnetic tunnel junction (MTJ) synapses, which performs image recognition via vector-matrix multiplication.
Analog Seizure Detection for Implanted Responsive Neurostimulation
Similarly, power calculations were performed, demonstrating that the system uses $6. 5 \mu W$ per channel, which when compared to the state-of-the-art NeuroPace system would increase battery life by up to $50 \%$.
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.
Reservoir Computing with Planar Nanomagnet Arrays
Reservoir computing is an emerging methodology for neuromorphic computing that is especially well-suited for hardware implementations in size, weight, and power (SWaP) constrained environments.
