- Intel’s system, called Hala Point, aims at supporting research for future brain-inspired AI
- Hala Point will be used by researchers for brain-scale computing research
- Hopes it will help AI systems to continuously learn from new data
Intel has announced that it has built the world’s largest neuromorphic system. Code-named Hala Point, this large-scale neuromorphic system aims to support research for future brain-inspired artificial intelligence (AI), and tackles challenges related to the efficiency and sustainability of AI.
Hala Point advances Intel’s first-generation large-scale research system, Pohoiki Springs, with architectural improvements to achieve over 10 times more neuron capacity and up to 12 times higher performance.
It is the first large-scale neuromorphic system to demonstrate state-of-the-art computational efficiencies on mainstream AI workloads. Hala Point’s capabilities could enable future real-time continuous learning for AI applications such as scientific and engineering problem-solving, logistics, smart city infrastructure management, large language models (LLMs) and AI agents.
“The computing cost of today’s AI models is rising at unsustainable rates,” says Mike Davies, director of the Neuromorphic Computing Lab at Intel Labs. “The industry needs fundamentally new approaches capable of scaling. For that reason, we developed Hala Point, which combines deep learning efficiency with novel brain-inspired learning and optimization capabilities. We hope that research with Hala Point will advance the efficiency and adaptability of large-scale AI technology.”
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.
