This review describes various types of low-power memristors, demonstrating their potential for a wide range of applications. This review summarizes low-power memristors for multi-level storage, ...

A recent study published in npj 2D Materials and Applications explores hexagonal boron nitride (h-BN) atomristors, highlighting their notable memory window, low leakage current, and minimal power ...

Benjamin Jungfleisch, associate professor of physics at the University of Delaware, uses this model of macroscopic spin-ice with permanent magnets to introduce magnetic interactions and phenomena to ...

Explore how neuromorphic chips and brain-inspired computing bring low-power, efficient intelligence to edge AI, robotics, and IoT through spiking neural networks and next-gen processors. Pixabay, ...

Scientists have discovered that electron spin loss, long considered waste, can instead drive magnetization switching in spintronic devices, boosting efficiency by up to three times. The scalable, ...

In an era where the rapid rise of artificial intelligence is accompanied by exponentially increasing energy costs, a promising approach involves harnessing ambient thermal noise as an ultra-low-power ...

The staggering computational demands of AI have become impossible to ignore. McKinsey estimates that training an AI model costs $4 million to $200 million per training run. The environmental impact is ...