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 ...

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, ...

Ag/Sb2O3/Au molecular-crystal memristor array with brain-inspired computing capabilities, designed to accelerate electric grid inspections while dramatically reducing energy consumption. The devices ...

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 ...

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, ...

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 ...

All-optical image processing has been viewed as a promising technique for its high computation speed and low power ...

At Computex in Taipei, Lokwon Kim, founder and CEO of the Korean semiconductor company DEEPX, shed light on his company's robust capabilities in designing high-performance AI chips that prioritize ...