Abstract Two-dimensional (2D) transition metal chalcogenides (TMC) and their
heterostructures are appealing as building blocks in a wide range of electronic and …

Artificial neural networks (ANNs) have experienced a rapid advancement for their success in
various application domains, including autonomous driving and drone vision. Researchers …

A number of recent efforts have attempted to design accelerators for popular machine
learning algorithms, such as those involving convolutional and deep neural networks (CNNs …

Spiking neural networks are being regarded as one of the promising alternative techniques
to overcome the high energy costs of artificial neural networks. It is supported by many …

As data processing volume increases, the limitations of traditional computers and the need
for more efficient computing methods become evident. Neuromorphic computing mimics the …

The spiking neural networks (SNNs), as brain-inspired neural networks, have received
noteworthy attention due to their advantages of low power consumption, high parallelism …

The outbreak of a global pandemic called coronavirus has created unprecedented
circumstances resulting into a large number of deaths and risk of community spreading …

Modelling functionalities of the brain in human-robot interaction contexts requires a real-time
understanding of how each part of a robot (motors, sensors, emotions, etc.) works and how …

Faster and cheaper computers have been constantly demanding technological and
architectural improvements. However, current technology is suffering from three technology …

Spiking Neural Network (SNN), originating from the neural behavior in biology, has been
recognized as one of the next-generation neural networks. Conventionally, SNNs can be …