Neural networks have enabled applications in artificial intelligence through machine
learning, and neuromorphic computing. Software implementations of neural networks on …

The exponential growth of information stored in data centers and computational power
required for various data-intensive applications, such as deep learning and AI, call for new …

Abstract Photonic Random-Access Memories (P-RAM) are an essential component for the
on-chip non-von Neumann photonic computing by eliminating optoelectronic conversion …

We demonstrate a passive all-chalcogenide all-optical perceptron scheme. The network's
nonlinear activation function (NLAF) relies on the nonlinear response of Ge2Sb2Te5 to …

Recent advances in neuromorphic computing have established a computational framework
that removes the processor-memory bottleneck evident in traditional von Neumann …

Abstract Ge-Sb-Se-Te (GSST) materials have recently emerged as an efficient phase
change material (PCM) that displays various exceptional properties essential for numerous …

The burgeoning of artificial intelligence has brought great convenience to people's lives as
large-scale computational models have emerged. Artificial intelligence-related applications …

The field of mimicking the structure of the brain on a chip is experiencing interest driven by
the demand for machine intelligent applications. However, the power consumption and …

We review different technologies and architectures for neuromorphic photonic accelerators,
spanning from bulk optics to photonic-integrated-circuits (PICs), and assess compute …

Phase change materials present a unique type of materials that drastically change their
electrical and optical properties on the introduction of an external electrical or optical …