Brain-inspired parallel computing, which is typically performed using a hardware neural-
network platform consisting of numerous artificial synapses, is a promising technology for …

On the basis of recent research, brain-inspired parallel computing is considered as one of
the most promising technologies for efficiently handling large amounts of informational data …

Neuromorphic systems (hardware neural networks) derive inspiration from biological neural
systems and are expected to be a computing breakthrough beyond conventional von …

The recent decline in energy, size and complexity scaling of traditional von Neumann
architecture has resurrected considerable interest in brain-inspired computing. Artificial …

Artificial neural networks (ANNs) based on synaptic devices, which can simultaneously
perform processing and storage of data, have superior computing performance compared to …

Many real-world mission-critical applications require continual online learning from noisy
data and real-time decision making with a defined confidence level. Brain-inspired …

Neuromorphic computing, an alternative for von Neumann architecture, requires synapse
devices where the data can be stored and computed in the same place. The three-terminal …

The rapid development of artificial intelligence (AI) demands the rapid development of
domain-specific hardware specifically designed for AI applications. Neuro-inspired …

Memristive synapses, the most promising passive devices for synaptic interconnections in
artificial neural networks, are the driving force behind recent research on hardware neural …

Analog machine learning hardware platforms promise to be faster and more energy efficient
than their digital counterparts. Wave physics, as found in acoustics and optics, is a natural …