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

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

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

The emergence of memristors with potential applications in data storage and artificial
intelligence has attracted wide attentions. Memristors are assembled in crossbar arrays with …

In the mammalian nervous system, various synaptic plasticity rules act, either individually or
synergistically, over wide-ranging timescales to enable learning and memory formation …

Recent years have witnessed a surge of interest in learning representations of graph-
structured data, with applications from social networks to drug discovery. However, graph …

As semiconductor technology enters the more than Moore era, there exists an apparent
contradiction between the rapidly growing demands for data processing and the visible …

Lifelong on-device learning is a key challenge for machine intelligence, and this requires
learning from few, often single, samples. Memory-augmented neural networks have been …

The growth of connected intelligent devices in the Internet of Things has created a pressing
need for real-time processing and understanding of large volumes of analogue data. The …

Due to the existence of membrane potential differences, the electromagnetic induction flows
can be induced in the interconnected neurons of Hopfield neural network (HNN). To express …