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Spiking Neural Classifier with Lumped Dendritic Nonlinearity and Binary Synapses: A Current Mode VLSI Implementation and Analysis.

Neural computation
We present a neuromorphic current mode implementation of a spiking neural classifier with lumped square law dendritic nonlinearity. It has been shown previously in software simulations that such a system with binary synapses can be trained with struc...

QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments.

PloS one
BACKGROUND: Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cyb...

Mimicking Biological Synaptic Functionality with an Indium Phosphide Synaptic Device on Silicon for Scalable Neuromorphic Computing.

ACS nano
Neuromorphic or "brain-like" computation is a leading candidate for efficient, fault-tolerant processing of large-scale data as well as real-time sensing and transduction of complex multivariate systems and networks such as self-driving vehicles or I...

Handwritten-Digit Recognition by Hybrid Convolutional Neural Network based on HfO Memristive Spiking-Neuron.

Scientific reports
Although there is a huge progress in complementary-metal-oxide-semiconductor (CMOS) technology, construction of an artificial neural network using CMOS technology to realize the functionality comparable with that of human cerebral cortex containing 1...

A 0.086-mm 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28-nm CMOS.

IEEE transactions on biomedical circuits and systems
Shifting computing architectures from von Neumann to event-based spiking neural networks (SNNs) uncovers new opportunities for low-power processing of sensory data in applications such as vision or sensorimotor control. Exploring roads toward cogniti...

Incoming Work-In-Progress Prediction in Semiconductor Fabrication Foundry Using Long Short-Term Memory.

Computational intelligence and neuroscience
Preventive maintenance activities require a tool to be offline for long hour in order to perform the prescribed maintenance activities. Although preventive maintenance is crucial to ensure operational reliability and efficiency of the tool, long hour...

RRAM-based synapse devices for neuromorphic systems.

Faraday discussions
Hardware artificial neural network (ANN) systems with high density synapse array devices can perform massive parallel computing for pattern recognition with low power consumption. To implement a neuromorphic system with on-chip training capability, w...

Artificial Sensory Memory.

Advanced materials (Deerfield Beach, Fla.)
Sensory memory, formed at the beginning while perceiving and interacting with the environment, is considered a primary source of intelligence. Transferring such biological concepts into electronic implementation aims at achieving perceptual intellige...

Electronic Skin: Recent Progress and Future Prospects for Skin-Attachable Devices for Health Monitoring, Robotics, and Prosthetics.

Advanced materials (Deerfield Beach, Fla.)
Recent progress in electronic skin or e-skin research is broadly reviewed, focusing on technologies needed in three main applications: skin-attachable electronics, robotics, and prosthetics. First, since e-skin will be exposed to prolonged stresses o...