Intel Builds World’s Largest Neuromorphic System to Enable More Sustainable AI - Seite 2

How It will be Used: Researchers at Sandia National Laboratories plan to use Hala Point for advanced brain-scale computing research. The organization will focus on solving scientific computing problems in device physics, computer architecture, computer science and informatics.

“Working with Hala Point improves our Sandia team’s capability to solve computational and scientific modeling problems. Conducting research with a system of this size will allow us to keep pace with AI’s evolution in fields ranging from commercial to defense to basic science,” said Craig Vineyard, Hala Point team lead at Sandia National Laboratories.

Currently, Hala Point is a research prototype that will advance the capabilities of future commercial systems. Intel anticipates that such lessons will lead to practical advancements, such as the ability for LLMs to learn continuously from new data. Such advancements promise to significantly reduce the unsustainable training burden of widespread AI deployments.

Why It Matters: Recent trends in scaling up deep learning models to trillions of parameters have exposed daunting sustainability challenges in AI and have highlighted the need for innovation at the lowest levels of hardware architecture. Neuromorphic computing is a fundamentally new approach that draws on neuroscience insights that integrate memory and computing with highly granular parallelism to minimize data movement. In published results from this month’s International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Loihi 2 demonstrated orders of magnitude gains in the efficiency, speed and adaptability of emerging small-scale edge workloads¹.

Advancing on its predecessor, Pohoiki Springs, with numerous improvements, Hala Point now brings neuromorphic performance and efficiency gains to mainstream conventional deep learning models, notably those processing real-time workloads such as video, speech and wireless communications. For example, Ericsson Research is applying Loihi 2 to optimize telecom infrastructure efficiency, as highlighted at this year’s Mobile World Congress.

About Hala Point: Loihi 2 neuromorphic processors, which form the basis for Hala Point, apply brain-inspired computing principles, such as asynchronous, event-based spiking neural networks (SNNs), integrated memory and computing, and sparse and continuously changing connections to achieve orders-of-magnitude gains in energy consumption and performance. Neurons communicate directly with one another rather than communicating through memory, reducing overall power consumption.