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, ...

Kawasaki and Yamaguchi, Japan, Nov 27, 2025 - (JCN Newswire) - - Fujitsu Limited and Yamaguchi University today announced the successful development of a low-power edge computing technology that ...

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 ...

DUBLIN--(BUSINESS WIRE)--The "The Global Market for Low Power/High Efficiency AI Semiconductors 2026-2036" has been added to ResearchAndMarkets.com's offering. The market for low power/high efficiency ...

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, ...

The demand for high-performance, energy-efficient computing hardware is growing rapidly, particularly in fields such as artificial intelligence and neuromorphic computing. Researchers have now ...

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 ...

Marvell has announced its Plug Computing initiative to make high-performance, always on, always connected, and environmentally friendly computing readily available for developers and end-users. A Plug ...

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 ...

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 ...