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 …

Field-effect transistors based on two-dimensional (2D) materials have the potential to be
used in very large-scale integration (VLSI) technology, but whether they can be used at the …

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

Realizing increasingly complex artificial intelligence (AI) functionalities directly on edge
devices calls for unprecedented energy efficiency of edge hardware. Compute-in-memory …

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 …

Porous crystalline materials usually include metal–organic frameworks (MOFs), covalent
organic frameworks (COFs), hydrogen-bonded organic frameworks (HOFs) and zeolites …

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

Neuromorphic computing memristors are attractive to construct low-power-consumption
electronic textiles due to the intrinsic interwoven architecture and promising applications in …

Neuromorphic computing aims at the realization of intelligent systems able to process
information similarly to our brain. Brain-inspired computing paradigms have been …