Search Results for author:
Found 15 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.
Synchronous Unsupervised STDP Learning with Stochastic STT-MRAM Switching
The use of analog resistance states for storing weights in neuromorphic systems is impeded by fabrication imprecision and device stochasticity that limit the precision of synapse weights.
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.
Shape-Dependent Multi-Weight Magnetic Artificial Synapses for Neuromorphic Computing
In neuromorphic computing, artificial synapses provide a multi-weight conductance state that is set based on inputs from neurons, analogous to the brain.
High-Speed CMOS-Free Purely Spintronic Asynchronous Recurrent Neural Network
Neuromorphic computing systems overcome the limitations of traditional von Neumann computing architectures.
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.
Controllable reset behavior in domain wall-magnetic tunnel junction artificial neurons for task-adaptable computation
Neuromorphic computing with spintronic devices has been of interest due to the limitations of CMOS-driven von Neumann computing.
Mesoscale and Nanoscale Physics
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.
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.
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.
Exploiting Dual-Gate Ambipolar CNFETs for Scalable Machine Learning Classification
Ambipolar carbon nanotube based field-effect transistors (AP-CNFETs) exhibit unique electrical characteristics, such as tri-state operation and bi-directionality, enabling systems with complex and reconfigurable computing.
