Modern computers are based on the von Neumann architecture in which computation and
storage are physically separated: data are fetched from the memory unit, shuttled to the …

In-memory computing (IMC) has emerged as a new computing paradigm able to alleviate or
suppress the memory bottleneck, which is the major concern for energy efficiency and …

Neural networks based on memristive devices,–have the ability to improve throughput and
energy efficiency for machine learning, and artificial intelligence, especially in edge …

This review addresses resistive switching devices operating according to the bipolar
valence change mechanism (VCM), which has become a major trend in electronic materials …

With the explosive growth of digital data in the era of the Internet of Things (IoT), fast and
scalable memory technologies are being researched for data storage and data-driven …

The low communication bandwidth between memory and processing units in conventional
von Neumann machines does not support the requirements of emerging applications that …

The human brain can perform advanced computing tasks, such as learning, recognition, and
cognition, with extremely low power consumption and low frequency of neuronal spiking …

Hardware artificial neural network (ANN) systems with high density synapse array devices
can perform massive parallel computing for pattern recognition with low power consumption …

In our brain, information is exchanged among neurons in the form of spikes where both the
space (which neuron fires) and time (when the neuron fires) contain relevant information …

HfO 2-based resistive switching memory (RRAM) combines several outstanding properties,
such as high scalability, fast switching speed, low power, compatibility with complementary …