Topological spin textures in magnetic materials and arrangements of electric dipoles in
ferroelectrics are considered to be promising candidates for next-generation information …

Physical reservoir computing is a form of neuromorphic computing that harvests the dynamic
properties of materials for high-efficiency computing. A wide range of physical systems can …

Physical neuromorphic computing, exploiting the complex dynamics of physical systems,
has seen rapid advancements in sophistication and performance. Physical reservoir …

Physical neural networks are artificial neural networks that mimic synapses and neurons
using physical systems or materials. These networks harness the distinctive characteristics …

Physical reservoir computing (RC) is a machine learning algorithm that employs the
dynamics of a physical system to forecast highly nonlinear and chaotic phenomena. In this …

Abstract Domain walls (DWs) in magnetic nanowires are promising candidates for a variety
of applications including Boolean/unconventional logic, memories, in-memory computing as …

With the advent of Big Data, traditional digital computing is struggling to cope with intricate
tasks related to data classification or pattern recognition. To mitigate this limitation, software …

Physical reservoirs are a promising approach for realizing high‐performance artificial
intelligence devices utilizing physical devices. Although nonlinear interfered spin‐wave …

Artificial intelligence (AI) systems of autonomous systems such as drones, robots and self-
driving cars may consume up to 50% of the total power available onboard, thereby limiting …

Reservoir computing (RC) is a bio-inspired neural network structure which can be
implemented in hardware with ease. It has been applied across various fields such as …