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

The rapid development of artificial intelligence (AI) demands the rapid development of
domain-specific hardware specifically designed for AI applications. Neuro-inspired …

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 …

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

Reservoir computing offers a powerful neuromorphic computing architecture for
spatiotemporal signal processing. To boost the power efficiency of the hardware …

Brain-inspired computing hardware aims to emulate the structure and working principles of
the brain and could be used to address current limitations in artificial intelligence …

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

The number of sensor nodes in the Internet of Things is growing rapidly, leading to a large
volume of data generated at sensory terminals. Frequent data transfer between the sensors …