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Found 12 papers, 0 papers with code
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.
On the Accuracy of Analog Neural Network Inference Accelerators
Specialized accelerators have recently garnered attention as a method to reduce the power consumption of neural network inference.
High-Speed CMOS-Free Purely Spintronic Asynchronous Recurrent Neural Network
Neuromorphic computing systems overcome the limitations of traditional von Neumann computing architectures.
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.
Device-aware inference operations in SONOS nonvolatile memory arrays
Non-volatile memory arrays can deploy pre-trained neural network models for edge inference.
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.
Evaluating complexity and resilience trade-offs in emerging memory inference machines
Neuromorphic-style inference only works well if limited hardware resources are maximized properly, e. g. accuracy continues to scale with parameters and complexity in the face of potential disturbance.
CMOS-Free Multilayer Perceptron Enabled by Four-Terminal MTJ Device
Neuromorphic computing promises revolutionary improvements over conventional systems for applications that process unstructured information.
Spatio-temporal Learning with Arrays of Analog Nanosynapses
By translating the database into the time domain and using variable integration windows, up to 95% classification accuracy is achieved.
Exploiting the Short-term to Long-term Plasticity Transition in Memristive Nanodevice Learning Architectures
This approach outperforms a conventional ELM-inspired system when the first layer is imprinted before training and testing, and especially so when variability in device timing evolution is considered: variability is therefore transformed from an issue to a feature.
