IBM’s NorthPole processor sidesteps need to access external memory, boosting computing power and saving energy.
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.” — Read More
Daily Archives: October 21, 2023
A new chip architecture points to faster, more energy-efficient AI
We’re in the midst of a Cambrian explosion in AI. Over the last decade, AI has gone from theory and small tests to enterprise-scale use cases. But the hardware used to run AI systems, although increasingly powerful, was not designed with today’s AI in mind. As AI systems scale, the costs skyrocket. And Moore’s Law, the theory that the density of circuits in processors would double each year, has slowed.
But new research out of IBM Research’s lab in Almaden, California, nearly two decades in the making, has the potential to drastically shift how we can efficiently scale up powerful AI hardware systems. — Read More
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Meta’s Habitat 3.0 simulates real-world environments for intelligent AI robot training
Researchers from Meta Platforms Inc.’s Fundamental Artificial Intelligence Research team said today they’re releasing a more advanced version of the AI simulation environment Habitat, which is used to teach robots how to interact with the physical world.
Along with the launch of Habitat 3.0, the company announced the release of the Habitat Synthetic Scenes Dataset, an artist-authored 3D dataset that can be used to train AI navigation agents, as well as HomeRobot, an affordable robot assistant hardware and software platform for use in both simulated and real world environments.
In a blog post, FAIR researchers explained that the new releases represent its ongoing progress into they like to call “embodied AI.” By that, they mean AI agents that can perceive and interact with their environment, share that environment safely with human partners, and communicate and assist those human partners in both the digital and the physical world. — Read More
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