Researchers developed an Ag/Sb2O3/Au memristor array that mimics brain-like computing, performing on-device image feature extraction with low power consumption, promising smarter and faster electric ...

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 ...

In recent years, power consumption by machine learning technologies, represented by deep learning and generative artificial ...

In a recent article published in Nature Communications, researchers from China presented a significant advancement in neuromorphic computing through the development of ultra-low-power carbon ...

A new neuromorphic chip has shown it can perform continuous voice activity detection while using extremely low power, ...

In a nutshell: The third keynote in the series of Computex CEO speeches was delivered by Arm's Rene Haas, along with Chris Bergey, the SVP and GM of the Client Business at the company. Their keynote ...

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 ...

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 ...