Spintronics and magnetic materials exhibit many physical phenomena that are promising for
implementing neuromorphic computing natively in hardware. Here, we review the current …

We report a breakthrough in the hardware implementation of energy-efficient all-spin
synapse and neuron devices for highly scalable integrated neuromorphic circuits. Our work …

Physical neural networks are artificial neural networks that mimic synapses and neurons
using physical systems or materials. These networks harness the distinctive characteristics …

With the rapid development of artificial intelligence in recent years, mankind is facing an
unprecedented demand for data processing. Today, almost all data processing is performed …

The quest to mimic the multistate synapses for bioinspired computing has triggered nascent
research that leverages the well-established magnetic tunnel junction (MTJ) technology …

In-materia reservoir computing (RC) leverages the intrinsic physical responses of functional
materials to perform complex computational tasks. Magnetic metamaterials are exciting …

The growing adoption of data-driven applications, such as artificial intelligence (AI), is
transforming the way we interact with technology. Currently, the deployment of AI and …

Energy efficiency in computation is ultimately limited by noise, with quantum limits setting the
fundamental noise floor. Analog physical neural networks hold promise for improved energy …

Physical neural networks (PNN) using physical materials and devices to mimic synapses
and neurons offer an energy-efficient way to implement artificial neural networks. Yet …

A practical limit to energy efficiency in computation is ultimately from noise, with quantum
noise [1] as the fundamental floor. Analog physical neural networks [2], which hold promise …