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 …

Synapses are essential for the transmission of neural signals. Synaptic plasticity allows for
changes in synaptic strength, enabling the brain to learn from experience. With the rapid …

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

In order to realize the full potential of wireless edge artificial intelligence (AI), very large and
diverse datasets will often be required for energy-demanding model training on resource …

Continued reduction in transistor size can improve the performance of silicon integrated
circuits (ICs). However, as Moore's law approaches physical limits, high‐performance …

In the emerging era of the internet of things (IoT), ubiquitous sensors continuously collect,
consume, store, and communicate a huge volume of information which is becoming …

Two-dimensional (2D) materials offer immense potential for scientific breakthroughs and
technological innovations. While early demonstrations of 2D material-based electronics …

Confronted by the difficulties of the von Neumann bottleneck and memory wall, traditional
computing systems are gradually inadequate for satisfying the demands of future data …

Since the isolation of graphene in 2004, two-dimensional (2D) materials research has
rapidly evolved into an entire subdiscipline in the physical sciences with a wide range of …

Spiking neural networks (SNNs) promise to bridge the gap between artificial neural
networks (ANNs) and biological neural networks (BNNs) by exploiting biologically plausible …