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

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

Neural networks based on memristive devices,–have the ability to improve throughput and
energy efficiency for machine learning, and artificial intelligence, especially in edge …

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

Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate
advanced electronic circuits is a major goal for the semiconductor industry,. However, most …

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

Memristive devices are gaining importance in the semiconductor industry for applications in
information storage, artificial intelligence cryptography and telecommunication. Memristive …

Memristive devices can offer dynamic behaviour, analogue programmability, and scaling
and integration capabilities. As a result, they are of potential use in the development of …

With the booming growth of artificial intelligence (AI), the traditional von Neumann
computing architecture based on complementary metal oxide semiconductor devices are …

New developments in hardware-based 'accelerators' range from electronic tensor cores and
memristor-based arrays to photonic implementations. The goal of these approaches is to …