Could computers ever learn more like humans do, without relying on artificial intelligence (AI) systems that must undergo ...

USC scientists design brain-like neurons that learn in hardware, not software, paving the way for energy-efficient AI general ...

As modern manufacturing increasingly relies on artificial intelligence (AI), automation, and real-time data processing, the need for faster and more energy-efficient computing systems has never been ...

The next competitive edge won’t come from brute force. It will come from systems that think more like markets themselves ...

Some heavy hitters like Intel, IBM, and Google along with a growing number of smaller startups for the past couple of decades have been pushing the development of neuromorphic computing, hardware that ...

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 ...

Scientists demonstrate neuromorphic computing utilizing perovskite microcavity exciton polaritons operating at room temperature. (Nanowerk News) Neuromorphic computing, inspired by the human brain, is ...

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 ...

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 ...

Neuromorphic computing -- a field that applies principles of neuroscience to computing systems to mimic the brain's function and structure -- needs to scale up if it is to effectively compete with ...