For how powerful today’s “smart” devices are, they’re not that good at working smarter rather than working harder. With AI constantly connected to the cloud and the chip constantly processing tasks ...

Brain-inspired computing promises cheaper, faster, more energy efficient processing, according to experts at a Beijing conference, who discussed everything from reverse engineering insect brains to ...

A team at University of Massachusetts Amherst developed artificial neurons that fire in the same voltage range as living ...

The NeuRRAM chip is not only twice as energy efficient as state-of-the-art, it's also versatile and delivers results that are just as accurate as conventional digital chips. Neuromorphic computing—a ...

Cory Merkel, assistant professor of computer engineering at Rochester Institute of Technology, will represent the university as one of five collegiate partners in the new Center of Neuromorphic ...

Large scale datasets and information processing requirements, within complex environments, are continuously reaching unprecedented levels of sophistication, especially in the advent of artificial ...

It’s estimated it can take an AI model over 6,000 joules of energy to generate a single text response. By comparison, your brain needs just 20 joules every second to keep you alive and cognitive. That ...

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, ...

What are spiking neural networks (SNNs)? Why neuromorphic computing is important. How BrainChip’s Akida platform brings neuromorphic computing to embedded applications. Artificial intelligence and ...

BUFFALO, N.Y. — It’s estimated it can take an AI model over 6,000 joules of energy to generate a single text response. By comparison, your brain needs just 20 joules every second to keep you alive and ...