Humans rely increasingly on sensors to address grand challenges and to improve quality of
life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are …

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 …

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

Magnetism is one of the largest, most fundamental, and technologically most relevant fields
of condensed-matter physics. Traditionally, two basic magnetic phases have been …

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 …

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

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 growing computational demand in artificial intelligence calls for hardware solutions that
are capable of in situ machine learning, where both training and inference are performed by …

In neuromorphic hardware, peripheral circuits and memories based on heterogeneous
devices are generally physically separated. Thus, exploration of homogeneous devices for …