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, ...

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, ...

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 ...

A research team has developed a device principle that can utilize "spin loss," which was previously thought of as a simple loss, as a new power source for magnetic control. Subscribe to our newsletter ...

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, ...

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 ...

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 ...

The SheevaPlug development platform is based on a Marvell Kirkwood processor and 1.2-GHz Sheeva CPU. The Plug Computing kit is equipped with 512 Mbytes of flash and 512 Mbytes of DRAM, and it has a ...