The volume, veracity, variability, and velocity of data produced from the ever increasing
network of sensors connected to Internet pose challenges for power management …

Multi-terminal memristor and memtransistor (MT-MEMs) has successfully performed
complex functions of heterosynaptic plasticity in synapse. However, theses MT-MEMs lack …

Resistive random access memory is very well known for its potential application in in-
memory and neural computing. However, they often have different types of device-to-device …

Although data processing technology continues to advance at an astonishing rate,
computers with brain-like processing capabilities still elude us. It is envisioned that such …

Neuromorphic is a relatively new interdisciplinary research topic, which employs various
fields of science and technology, such as electronic, computer, and biology. Neuromorphic …

Neuromorphic systems using memristors as artificial synapses have attracted broad interest
for energy-efficient computing applications. However, networks based on these purely …

The brain's function can be dynamically reconfigured through a unified neuron–synapse
architecture, enabling task-adaptive network-level topology for energy-efficient learning and …

Neuromorphic computing has become an attractive candidate for emerging computing
platforms. It requires an architectural perspective, meaning the topology or hyperparameters …

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

Computation on a large volume of data at high speed and low power requires energy-
efficient architectures for edge computing applications. As a result, scientists focus on …