Memristive devices, which combine a resistor with memory functions such that voltage
pulses can change their resistance (and hence their memory state) in a nonvolatile manner …

Memristive technology has been rapidly emerging as a potential alternative to traditional
CMOS technology, which is facing fundamental limitations in its development. Since oxide …

Neuromorphic computing based on emerging devices could overcome the von Neumann
bottleneck—the restriction created by having to transfer data between memory and …

Implementations of artificial neural networks that borrow analogue techniques could
potentially offer low-power alternatives to fully digital approaches,–. One notable example is …

Ferroelectric materials, the charge equivalent of magnets, have been the subject of
continued research interest since their discovery more than 100 years ago. The …

Rationally designed architected materials have attained previously untapped territories in
materials property space. The properties and behaviours of architected materials need not …

Learning is highly important for edge intelligence devices to adapt to different application
scenes and owners. Current technologies for training neural networks require moving …

Memristors in electronics have shown the potential for a range of applications, ranging from
circuit elements to neuromorphic computing. In recent years, the ability to vary the …

Analogue in-memory computing (AIMC) with resistive memory devices could reduce the
latency and energy consumption of deep neural network inference tasks by directly …

Three-dimensional monolithic integration of memory devices with logic transistors is a
frontier challenge in computer hardware. This integration is essential for augmenting …