When you buy through links on our articles, Future and its syndication partners may earn a commission. Although neuromorphic computing was first proposed by scientist Carver Mead in the late 1980s, it ...
New research shows that advances in technology could help make future supercomputers far more energy efficient. Neuromorphic computers are modeled after the structure of the human brain, and researche ...
IEEE Spectrum on MSN
Brain-inspired Computing Is Ready for the Big Time
Efforts to build brain-inspired computer hardware have been underway for decades, but the field has yet to have its breakout moment. Now, leading researchers say the time is ripe to start building the ...
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 ...
Joseph Friedman, associate professor of electrical and computer engineering at the University of Texas at Dallas, uses a probe station to test small neuromorphic devices. Friedman has developed a ...
The Register on MSN
Artificial brains could point the way to ultra-efficient supercomputers
Sandia National Labs cajole Intel's neurochips into solving partial differential equations New research from Sandia National ...
Rochester Institute of Technology recently became one of the inaugural academic partners in the BrainChip University AI Accelerator Program. As part of the university-corporate partnership, RIT’s ...
As artificial intelligence platforms like OpenAI’s ChatGPT and Microsoft’s Copilot go mainstream, power bills from their usage are exploding. In response, researchers are racing to build hardware that ...
An interdisciplinary team of researchers are working on a radically new kind of computer called a neuromorphic computer, inspired by the human brain. Mock-up of a quantum photonic device, which could ...
Dr. Joseph S. Friedman and his colleagues at The University of Texas at Dallas created a computer prototype that learns patterns and makes predictions using fewer training computations than ...
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