Bioinspired computation systems can achieve artificial intelligence, bypassing fundamental
bottlenecks and cost constraints. Computational frameworks suited for temporal/sequential …

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 …

Emerging memories have been developed as new physical infrastructures for hosting neural
networks owing to their low-power analog computing characteristics. However, accurately …

Nonlinear phenomena in physical systems can be used for brain-inspired computing with
low energy consumption. Response from the dynamics of a topological spin structure called …

Neuromorphic computing (NC) architecture inspired by biological nervous systems has
been actively studied to overcome the limitations of conventional von Neumann …

Memristor‐based physical reservoir computing (RC) is a robust framework for processing
complex spatiotemporal data parallelly. However, conventional memristor‐based reservoirs …

Reservoir computing can more efficiently be used to solve time-dependent tasks than
conventional feedforward network owing to various advantages, such as easy training and …

Nowadays, neuromorphic systems based on memristors are considered promising
approaches to the hardware realization of artificial intelligence systems with efficient …

The booming development of artificial intelligence (AI) requires faster physical processing
units as well as more efficient algorithms. Recently, reservoir computing (RC) has emerged …

A physical reservoir device with tunable transient dynamics is strongly required to process
time-series data with various timescales generated in the edge region. In this study, we …