Oct 21, 2023 07:48 PM
https://www.nature.com/articles/d41586-023-03267-0
INTRO: A brain-inspired computer chip that could supercharge artificial intelligence (AI) by working faster with much less power has been developed by researchers at IBM in San Jose, California. Their massive NorthPole processor chip eliminates the need to frequently access external memory, and so performs tasks such as image recognition faster than existing architectures do — while consuming vastly less power.
“Its energy efficiency is just mind-blowing,” says Damien Querlioz, a nanoelectronics researcher at the University of Paris-Saclay in Palaiseau. The work, published in Science1, shows that computing and memory can be integrated on a large scale, he says. “I feel the paper will shake the common thinking in computer architecture.”
NorthPole runs neural networks: multi-layered arrays of simple computational units programmed to recognize patterns in data. A bottom layer takes in data, such as the pixels in an image; each successive layer detects patterns of increasing complexity and passes information on to the next layer. The top layer produces an output that, for example, can express how likely an image is to contain a cat, a car or other objects... (MORE - details)
INTRO: A brain-inspired computer chip that could supercharge artificial intelligence (AI) by working faster with much less power has been developed by researchers at IBM in San Jose, California. Their massive NorthPole processor chip eliminates the need to frequently access external memory, and so performs tasks such as image recognition faster than existing architectures do — while consuming vastly less power.
“Its energy efficiency is just mind-blowing,” says Damien Querlioz, a nanoelectronics researcher at the University of Paris-Saclay in Palaiseau. The work, published in Science1, shows that computing and memory can be integrated on a large scale, he says. “I feel the paper will shake the common thinking in computer architecture.”
NorthPole runs neural networks: multi-layered arrays of simple computational units programmed to recognize patterns in data. A bottom layer takes in data, such as the pixels in an image; each successive layer detects patterns of increasing complexity and passes information on to the next layer. The top layer produces an output that, for example, can express how likely an image is to contain a cat, a car or other objects... (MORE - details)
