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

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 …

Speech recognition has become an important task to improve the human-machine interface.
Taking into account the limitations of current automatic speech recognition systems, like non …

Hybrid electrical/optical (E/O) switching data center network (DCN) has recently emerged as
a promising paradigm for future DCN architectures. However, there exist two major …